Scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction to Web Scraping with BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlopen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_url=\"https://en.wikipedia.org/wiki/Generative_adversarial_network\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gans_info=urlopen(dl_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "http.client.HTTPResponse"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gans_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "gans_html=gans_info.read()\n",
    "# print(gans_html)\n",
    "gans_info.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as soup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For parsing the html, the page object needs to be passed to BeautifulSoup.\n",
    "gans_soup=soup(gans_html,'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "<h1 class=\"firstHeading\" id=\"firstHeading\" lang=\"en\">Generative adversarial network</h1>\n",
       "<div class=\"mw-body-content\" id=\"bodyContent\">\n",
       "<div class=\"noprint\" id=\"siteSub\">From Wikipedia, the free encyclopedia</div>\n",
       "<div id=\"contentSub\"></div>\n",
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       "<div class=\"mw-content-ltr\" dir=\"ltr\" id=\"mw-content-text\" lang=\"en\"><div class=\"mw-parser-output\"><div class=\"hatnote navigation-not-searchable\" role=\"note\">Not to be confused with <a href=\"/wiki/Adversarial_machine_learning\" title=\"Adversarial machine learning\">Adversarial machine learning</a>.</div>\n",
       "<table class=\"vertical-navbox nowraplinks\" style=\"float:right;clear:right;width:22.0em;margin:0 0 1.0em 1.0em;background:#f9f9f9;border:1px solid #aaa;padding:0.2em;border-spacing:0.4em 0;text-align:center;line-height:1.4em;font-size:88%\"><tbody><tr><th style=\"padding:0.2em 0.4em 0.2em;font-size:145%;line-height:1.2em\"><a href=\"/wiki/Machine_learning\" title=\"Machine learning\">Machine learning</a> and<br/><a href=\"/wiki/Data_mining\" title=\"Data mining\">data mining</a></th></tr><tr><td style=\"padding:0.2em 0 0.4em;padding:0.25em 0.25em 0.75em;\"><a class=\"image\" href=\"/wiki/File:Kernel_Machine.svg\"><img alt=\"Kernel Machine.svg\" data-file-height=\"233\" data-file-width=\"512\" decoding=\"async\" height=\"100\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/220px-Kernel_Machine.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/330px-Kernel_Machine.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Kernel_Machine.svg/440px-Kernel_Machine.svg.png 2x\" width=\"220\"/></a></td></tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\">Problems</div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Statistical_classification\" title=\"Statistical classification\">Classification</a></li>\n",
       "<li><a href=\"/wiki/Cluster_analysis\" title=\"Cluster analysis\">Clustering</a></li>\n",
       "<li><a href=\"/wiki/Regression_analysis\" title=\"Regression analysis\">Regression</a></li>\n",
       "<li><a href=\"/wiki/Anomaly_detection\" title=\"Anomaly detection\">Anomaly detection</a></li>\n",
       "<li><a href=\"/wiki/Automated_machine_learning\" title=\"Automated machine learning\">AutoML</a></li>\n",
       "<li><a href=\"/wiki/Association_rule_learning\" title=\"Association rule learning\">Association rules</a></li>\n",
       "<li><a href=\"/wiki/Reinforcement_learning\" title=\"Reinforcement learning\">Reinforcement learning</a></li>\n",
       "<li><a href=\"/wiki/Structured_prediction\" title=\"Structured prediction\">Structured prediction</a></li>\n",
       "<li><a href=\"/wiki/Feature_engineering\" title=\"Feature engineering\">Feature engineering</a></li>\n",
       "<li><a href=\"/wiki/Feature_learning\" title=\"Feature learning\">Feature learning</a></li>\n",
       "<li><a href=\"/wiki/Online_machine_learning\" title=\"Online machine learning\">Online learning</a></li>\n",
       "<li><a href=\"/wiki/Semi-supervised_learning\" title=\"Semi-supervised learning\">Semi-supervised learning</a></li>\n",
       "<li><a href=\"/wiki/Unsupervised_learning\" title=\"Unsupervised learning\">Unsupervised learning</a></li>\n",
       "<li><a href=\"/wiki/Learning_to_rank\" title=\"Learning to rank\">Learning to rank</a></li>\n",
       "<li><a href=\"/wiki/Grammar_induction\" title=\"Grammar induction\">Grammar induction</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><div style=\"padding:0.1em 0;line-height:1.2em;\"><a href=\"/wiki/Supervised_learning\" title=\"Supervised learning\">Supervised learning</a><br/><style data-mw-deduplicate=\"TemplateStyles:r886047488\">.mw-parser-output .nobold{font-weight:normal}</style><span class=\"nobold\"><span style=\"font-size:85%;\">(<b><a href=\"/wiki/Statistical_classification\" title=\"Statistical classification\">classification</a></b> • <b><a href=\"/wiki/Regression_analysis\" title=\"Regression analysis\">regression</a></b>)</span></span> </div></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Decision_tree_learning\" title=\"Decision tree learning\">Decision trees</a></li>\n",
       "<li><a href=\"/wiki/Ensemble_learning\" title=\"Ensemble learning\">Ensembles</a>\n",
       "<ul><li><a href=\"/wiki/Bootstrap_aggregating\" title=\"Bootstrap aggregating\">Bagging</a></li>\n",
       "<li><a href=\"/wiki/Boosting_(machine_learning)\" title=\"Boosting (machine learning)\">Boosting</a></li>\n",
       "<li><a href=\"/wiki/Random_forest\" title=\"Random forest\">Random forest</a></li></ul></li>\n",
       "<li><a href=\"/wiki/K-nearest_neighbors_algorithm\" title=\"K-nearest neighbors algorithm\"><i>k</i>-NN</a></li>\n",
       "<li><a href=\"/wiki/Linear_regression\" title=\"Linear regression\">Linear regression</a></li>\n",
       "<li><a href=\"/wiki/Naive_Bayes_classifier\" title=\"Naive Bayes classifier\">Naive Bayes</a></li>\n",
       "<li><a href=\"/wiki/Artificial_neural_network\" title=\"Artificial neural network\">Artificial neural networks</a></li>\n",
       "<li><a href=\"/wiki/Logistic_regression\" title=\"Logistic regression\">Logistic regression</a></li>\n",
       "<li><a href=\"/wiki/Perceptron\" title=\"Perceptron\">Perceptron</a></li>\n",
       "<li><a href=\"/wiki/Relevance_vector_machine\" title=\"Relevance vector machine\">Relevance vector machine (RVM)</a></li>\n",
       "<li><a href=\"/wiki/Support-vector_machine\" title=\"Support-vector machine\">Support vector machine (SVM)</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Cluster_analysis\" title=\"Cluster analysis\">Clustering</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/BIRCH\" title=\"BIRCH\">BIRCH</a></li>\n",
       "<li><a class=\"mw-redirect\" href=\"/wiki/CURE_data_clustering_algorithm\" title=\"CURE data clustering algorithm\">CURE</a></li>\n",
       "<li><a href=\"/wiki/Hierarchical_clustering\" title=\"Hierarchical clustering\">Hierarchical</a></li>\n",
       "<li><a href=\"/wiki/K-means_clustering\" title=\"K-means clustering\"><i>k</i>-means</a></li>\n",
       "<li><a href=\"/wiki/Expectation%E2%80%93maximization_algorithm\" title=\"Expectation–maximization algorithm\">Expectation–maximization (EM)</a></li>\n",
       "<li><br/><a href=\"/wiki/DBSCAN\" title=\"DBSCAN\">DBSCAN</a></li>\n",
       "<li><a href=\"/wiki/OPTICS_algorithm\" title=\"OPTICS algorithm\">OPTICS</a></li>\n",
       "<li><a class=\"mw-redirect\" href=\"/wiki/Mean-shift\" title=\"Mean-shift\">Mean-shift</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Dimensionality_reduction\" title=\"Dimensionality reduction\">Dimensionality reduction</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Factor_analysis\" title=\"Factor analysis\">Factor analysis</a></li>\n",
       "<li><a class=\"mw-redirect\" href=\"/wiki/Canonical_correlation_analysis\" title=\"Canonical correlation analysis\">CCA</a></li>\n",
       "<li><a href=\"/wiki/Independent_component_analysis\" title=\"Independent component analysis\">ICA</a></li>\n",
       "<li><a href=\"/wiki/Linear_discriminant_analysis\" title=\"Linear discriminant analysis\">LDA</a></li>\n",
       "<li><a href=\"/wiki/Non-negative_matrix_factorization\" title=\"Non-negative matrix factorization\">NMF</a></li>\n",
       "<li><a href=\"/wiki/Principal_component_analysis\" title=\"Principal component analysis\">PCA</a></li>\n",
       "<li><a href=\"/wiki/T-distributed_stochastic_neighbor_embedding\" title=\"T-distributed stochastic neighbor embedding\">t-SNE</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Structured_prediction\" title=\"Structured prediction\">Structured prediction</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Graphical_model\" title=\"Graphical model\">Graphical models</a>\n",
       "<ul><li><a href=\"/wiki/Bayesian_network\" title=\"Bayesian network\">Bayes net</a></li>\n",
       "<li><a href=\"/wiki/Conditional_random_field\" title=\"Conditional random field\">Conditional random field</a></li>\n",
       "<li><a href=\"/wiki/Hidden_Markov_model\" title=\"Hidden Markov model\">Hidden Markov</a></li></ul></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Anomaly_detection\" title=\"Anomaly detection\">Anomaly detection</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a class=\"mw-redirect\" href=\"/wiki/K-nearest_neighbors_classification\" title=\"K-nearest neighbors classification\"><i>k</i>-NN</a></li>\n",
       "<li><a href=\"/wiki/Local_outlier_factor\" title=\"Local outlier factor\">Local outlier factor</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a class=\"mw-redirect\" href=\"/wiki/Artificial_neural_networks\" title=\"Artificial neural networks\">Artificial neural networks</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Autoencoder\" title=\"Autoencoder\">Autoencoder</a></li>\n",
       "<li><a href=\"/wiki/Deep_learning\" title=\"Deep learning\">Deep learning</a></li>\n",
       "<li><a href=\"/wiki/DeepDream\" title=\"DeepDream\">DeepDream</a></li>\n",
       "<li><a href=\"/wiki/Multilayer_perceptron\" title=\"Multilayer perceptron\">Multilayer perceptron</a></li>\n",
       "<li><a href=\"/wiki/Recurrent_neural_network\" title=\"Recurrent neural network\">RNN</a>\n",
       "<ul><li><a href=\"/wiki/Long_short-term_memory\" title=\"Long short-term memory\">LSTM</a></li>\n",
       "<li><a href=\"/wiki/Gated_recurrent_unit\" title=\"Gated recurrent unit\">GRU</a></li></ul></li>\n",
       "<li><a href=\"/wiki/Restricted_Boltzmann_machine\" title=\"Restricted Boltzmann machine\">Restricted Boltzmann machine</a></li>\n",
       "<li><a class=\"mw-selflink selflink\">GAN</a></li>\n",
       "<li><a href=\"/wiki/Self-organizing_map\" title=\"Self-organizing map\">SOM</a></li>\n",
       "<li><a href=\"/wiki/Convolutional_neural_network\" title=\"Convolutional neural network\">Convolutional neural network</a>\n",
       "<ul><li><a href=\"/wiki/U-Net\" title=\"U-Net\">U-Net</a></li></ul></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Reinforcement_learning\" title=\"Reinforcement learning\">Reinforcement learning</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Q-learning\" title=\"Q-learning\">Q-learning</a></li>\n",
       "<li><a href=\"/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action\" title=\"State–action–reward–state–action\">SARSA</a></li>\n",
       "<li><a href=\"/wiki/Temporal_difference_learning\" title=\"Temporal difference learning\">Temporal difference (TD)</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\">Theory</div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a class=\"mw-redirect\" href=\"/wiki/Bias%E2%80%93variance_dilemma\" title=\"Bias–variance dilemma\">Bias–variance dilemma</a></li>\n",
       "<li><a href=\"/wiki/Computational_learning_theory\" title=\"Computational learning theory\">Computational learning theory</a></li>\n",
       "<li><a href=\"/wiki/Empirical_risk_minimization\" title=\"Empirical risk minimization\">Empirical risk minimization</a></li>\n",
       "<li><a href=\"/wiki/Occam_learning\" title=\"Occam learning\">Occam learning</a></li>\n",
       "<li><a href=\"/wiki/Probably_approximately_correct_learning\" title=\"Probably approximately correct learning\">PAC learning</a></li>\n",
       "<li><a href=\"/wiki/Statistical_learning_theory\" title=\"Statistical learning theory\">Statistical learning</a></li>\n",
       "<li><a href=\"/wiki/Vapnik%E2%80%93Chervonenkis_theory\" title=\"Vapnik–Chervonenkis theory\">VC theory</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\">Machine-learning venues</div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Conference_on_Neural_Information_Processing_Systems\" title=\"Conference on Neural Information Processing Systems\">NIPS</a></li>\n",
       "<li><a href=\"/wiki/International_Conference_on_Machine_Learning\" title=\"International Conference on Machine Learning\">ICML</a></li>\n",
       "<li><a href=\"/wiki/Machine_Learning_(journal)\" title=\"Machine Learning (journal)\">ML</a></li>\n",
       "<li><a href=\"/wiki/Journal_of_Machine_Learning_Research\" title=\"Journal of Machine Learning Research\">JMLR</a></li>\n",
       "<li><a class=\"external text\" href=\"https://arxiv.org/list/cs.LG/recent\" rel=\"nofollow\">ArXiv:cs.LG</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\"><a href=\"/wiki/Glossary_of_artificial_intelligence\" title=\"Glossary of artificial intelligence\">Glossary of artificial intelligence</a></div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/Glossary_of_artificial_intelligence\" title=\"Glossary of artificial intelligence\">Glossary of artificial intelligence</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"padding:0 0.1em 0.4em\">\n",
       "<div class=\"NavFrame collapsed\" style=\"border:none;padding:0\"><div class=\"NavHead\" style=\"font-size:105%;background:transparent;text-align:left\">Related articles</div><div class=\"NavContent\" style=\"font-size:105%;padding:0.2em 0 0.4em;text-align:center\"><div class=\"hlist\">\n",
       "<ul><li><a href=\"/wiki/List_of_datasets_for_machine-learning_research\" title=\"List of datasets for machine-learning research\">List of datasets for machine-learning research</a></li>\n",
       "<li><a href=\"/wiki/Outline_of_machine_learning\" title=\"Outline of machine learning\">Outline of machine learning</a></li></ul>\n",
       "</div></div></div></td>\n",
       "</tr><tr><td style=\"text-align:right;font-size:115%;padding-top: 0.6em;\"><div class=\"plainlinks hlist navbar mini\"><ul><li class=\"nv-view\"><a href=\"/wiki/Template:Machine_learning_bar\" title=\"Template:Machine learning bar\"><abbr title=\"View this template\">v</abbr></a></li><li class=\"nv-talk\"><a href=\"/wiki/Template_talk:Machine_learning_bar\" title=\"Template talk:Machine learning bar\"><abbr title=\"Discuss this template\">t</abbr></a></li><li class=\"nv-edit\"><a class=\"external text\" href=\"//en.wikipedia.org/w/index.php?title=Template:Machine_learning_bar&amp;action=edit\"><abbr title=\"Edit this template\">e</abbr></a></li></ul></div></td></tr></tbody></table>\n",
       "<p>A <b>generative adversarial network</b> (<b>GAN</b>) is a class of <a href=\"/wiki/Machine_learning\" title=\"Machine learning\">machine learning</a> systems invented by <a href=\"/wiki/Ian_Goodfellow\" title=\"Ian Goodfellow\">Ian Goodfellow</a> and his colleagues in 2014.<sup class=\"reference\" id=\"cite_ref-GANnips_1-0\"><a href=\"#cite_note-GANnips-1\">[1]</a></sup> Two <a href=\"/wiki/Neural_network\" title=\"Neural network\">neural networks</a> contest with each other in a game (in the sense of <a href=\"/wiki/Game_theory\" title=\"Game theory\">game theory</a>, often but not always in the form of a <a href=\"/wiki/Zero-sum_game\" title=\"Zero-sum game\">zero-sum game</a>). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of <a href=\"/wiki/Generative_model\" title=\"Generative model\">generative model</a> for <a href=\"/wiki/Unsupervised_learning\" title=\"Unsupervised learning\">unsupervised learning</a>, GANs have also proven useful for <a href=\"/wiki/Semi-supervised_learning\" title=\"Semi-supervised learning\">semi-supervised learning</a>,<sup class=\"reference\" id=\"cite_ref-ITT_GANs_2-0\"><a href=\"#cite_note-ITT_GANs-2\">[2]</a></sup> fully <a href=\"/wiki/Supervised_learning\" title=\"Supervised learning\">supervised learning</a>,<sup class=\"reference\" id=\"cite_ref-3\"><a href=\"#cite_note-3\">[3]</a></sup> and <a href=\"/wiki/Reinforcement_learning\" title=\"Reinforcement learning\">reinforcement learning</a>.<sup class=\"reference\" id=\"cite_ref-4\"><a href=\"#cite_note-4\">[4]</a></sup> In a 2016 seminar, <a href=\"/wiki/Yann_LeCun\" title=\"Yann LeCun\">Yann LeCun</a> described GANs as \"the coolest idea in machine learning in the last twenty years\".<sup class=\"reference\" id=\"cite_ref-5\"><a href=\"#cite_note-5\">[5]</a></sup>\n",
       "</p>\n",
       "<div class=\"toc\" id=\"toc\"><input class=\"toctogglecheckbox\" id=\"toctogglecheckbox\" role=\"button\" style=\"display:none\" type=\"checkbox\"/><div class=\"toctitle\" dir=\"ltr\" lang=\"en\"><h2>Contents</h2><span class=\"toctogglespan\"><label class=\"toctogglelabel\" for=\"toctogglecheckbox\"></label></span></div>\n",
       "<ul>\n",
       "<li class=\"toclevel-1 tocsection-1\"><a href=\"#Method\"><span class=\"tocnumber\">1</span> <span class=\"toctext\">Method</span></a></li>\n",
       "<li class=\"toclevel-1 tocsection-2\"><a href=\"#Applications\"><span class=\"tocnumber\">2</span> <span class=\"toctext\">Applications</span></a>\n",
       "<ul>\n",
       "<li class=\"toclevel-2 tocsection-3\"><a href=\"#Fashion_and_advertising\"><span class=\"tocnumber\">2.1</span> <span class=\"toctext\">Fashion and advertising</span></a></li>\n",
       "<li class=\"toclevel-2 tocsection-4\"><a href=\"#Science\"><span class=\"tocnumber\">2.2</span> <span class=\"toctext\">Science</span></a></li>\n",
       "<li class=\"toclevel-2 tocsection-5\"><a href=\"#Video_games\"><span class=\"tocnumber\">2.3</span> <span class=\"toctext\">Video games</span></a></li>\n",
       "<li class=\"toclevel-2 tocsection-6\"><a href=\"#Concerns_about_malicious_applications\"><span class=\"tocnumber\">2.4</span> <span class=\"toctext\">Concerns about malicious applications</span></a></li>\n",
       "<li class=\"toclevel-2 tocsection-7\"><a href=\"#Miscellaneous_applications\"><span class=\"tocnumber\">2.5</span> <span class=\"toctext\">Miscellaneous applications</span></a></li>\n",
       "</ul>\n",
       "</li>\n",
       "<li class=\"toclevel-1 tocsection-8\"><a href=\"#History\"><span class=\"tocnumber\">3</span> <span class=\"toctext\">History</span></a></li>\n",
       "<li class=\"toclevel-1 tocsection-9\"><a href=\"#References\"><span class=\"tocnumber\">4</span> <span class=\"toctext\">References</span></a></li>\n",
       "<li class=\"toclevel-1 tocsection-10\"><a href=\"#External_links\"><span class=\"tocnumber\">5</span> <span class=\"toctext\">External links</span></a></li>\n",
       "</ul>\n",
       "</div>\n",
       "<h2><span class=\"mw-headline\" id=\"Method\">Method</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=1\" title=\"Edit section: Method\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h2>\n",
       "<p>The <a href=\"/wiki/Generative_model\" title=\"Generative model\"><i>generative</i> network</a> generates candidates while the <a href=\"/wiki/Discriminative_model\" title=\"Discriminative model\"><i>discriminative</i> network</a> evaluates them.<sup class=\"reference\" id=\"cite_ref-GANnips_1-1\"><a href=\"#cite_note-GANnips-1\">[1]</a></sup> The contest operates in terms of data distributions. Typically, the generative network learns to map from a <a href=\"/wiki/Latent_variable\" title=\"Latent variable\">latent space</a> to a data distribution of interest, while the discriminative network distinguishes candidates produced by the generator from the true data distribution. The generative network's training objective is to increase the error rate of the discriminative network (i.e., \"fool\" the discriminator network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution)).<sup class=\"reference\" id=\"cite_ref-GANnips_1-2\"><a href=\"#cite_note-GANnips-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-6\"><a href=\"#cite_note-6\">[6]</a></sup>\n",
       "</p><p>A known dataset serves as the initial training data for the discriminator. Training it involves presenting it with samples from the training dataset, until it achieves acceptable accuracy. The generator trains based on whether it succeeds in fooling the discriminator. Typically the generator is seeded with randomized input that is sampled from a predefined <a href=\"/wiki/Latent_variable\" title=\"Latent variable\">latent space</a> (e.g. a <a href=\"/wiki/Multivariate_normal_distribution\" title=\"Multivariate normal distribution\">multivariate normal distribution</a>). Thereafter, candidates synthesized by the generator are evaluated by the discriminator. <a href=\"/wiki/Backpropagation\" title=\"Backpropagation\">Backpropagation</a> is applied in both networks so that the generator produces better images, while the discriminator becomes more skilled at flagging synthetic images.<sup class=\"reference\" id=\"cite_ref-OpenAI_com_7-0\"><a href=\"#cite_note-OpenAI_com-7\">[7]</a></sup> The generator is typically a deconvolutional neural network, and the discriminator is a <a href=\"/wiki/Convolutional_neural_network\" title=\"Convolutional neural network\">convolutional neural network</a>.\n",
       "</p>\n",
       "<h2><span class=\"mw-headline\" id=\"Applications\">Applications</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=2\" title=\"Edit section: Applications\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h2>\n",
       "<p>GAN applications have increased rapidly.<sup class=\"reference\" id=\"cite_ref-8\"><a href=\"#cite_note-8\">[8]</a></sup>\n",
       "</p>\n",
       "<h3><span class=\"mw-headline\" id=\"Fashion_and_advertising\">Fashion and advertising</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=3\" title=\"Edit section: Fashion and advertising\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h3>\n",
       "<p>GANs can be used to create photos of imaginary fashion models, with no need to hire a model, photographer, makeup artist, or pay for a studio and transportation.<sup class=\"reference\" id=\"cite_ref-9\"><a href=\"#cite_note-9\">[9]</a></sup> GANs can be used to create fashion advertising campaigns including more diverse groups of models, which may increase intent to buy among people resembling the models.<sup class=\"reference\" id=\"cite_ref-10\"><a href=\"#cite_note-10\">[10]</a></sup>\n",
       "</p>\n",
       "<h3><span class=\"mw-headline\" id=\"Science\">Science</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=4\" title=\"Edit section: Science\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h3>\n",
       "<p>GANs can <a href=\"/wiki/Image_restoration\" title=\"Image restoration\">improve</a> <a href=\"/wiki/Astrophotography\" title=\"Astrophotography\">astronomical images</a><sup class=\"reference\" id=\"cite_ref-11\"><a href=\"#cite_note-11\">[11]</a></sup> and simulate gravitational lensing for dark matter research.<sup class=\"reference\" id=\"cite_ref-12\"><a href=\"#cite_note-12\">[12]</a></sup><sup class=\"reference\" id=\"cite_ref-13\"><a href=\"#cite_note-13\">[13]</a></sup><sup class=\"reference\" id=\"cite_ref-14\"><a href=\"#cite_note-14\">[14]</a></sup> They were used in 2019 to successfully model the distribution of <a href=\"/wiki/Dark_matter\" title=\"Dark matter\">dark matter</a> in a particular direction in space and to predict the <a class=\"mw-redirect\" href=\"/wiki/Gravitational_Lensing\" title=\"Gravitational Lensing\">gravitational lensing</a> that will occur.<sup class=\"reference\" id=\"cite_ref-15\"><a href=\"#cite_note-15\">[15]</a></sup><sup class=\"reference\" id=\"cite_ref-16\"><a href=\"#cite_note-16\">[16]</a></sup>\n",
       "</p><p>GANs have also been proposed as a fast and accurate way of generating simulated <a href=\"/wiki/Particle_shower\" title=\"Particle shower\">showers</a> of particles in the <a href=\"/wiki/Calorimeter_(particle_physics)\" title=\"Calorimeter (particle physics)\">calorimeters</a> of <a href=\"/wiki/Particle_physics\" title=\"Particle physics\">high-energy physics</a> experiments.<sup class=\"reference\" id=\"cite_ref-17\"><a href=\"#cite_note-17\">[17]</a></sup><sup class=\"reference\" id=\"cite_ref-18\"><a href=\"#cite_note-18\">[18]</a></sup>\n",
       "</p>\n",
       "<h3><span class=\"mw-headline\" id=\"Video_games\">Video games</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=5\" title=\"Edit section: Video games\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h3>\n",
       "<p>In 2018, GANs reached the <a href=\"/wiki/Mod_(video_gaming)\" title=\"Mod (video gaming)\">video game modding</a> community, as a method of <a href=\"/wiki/Image_scaling\" title=\"Image scaling\">up-scaling</a> low-resolution 2D textures in old video games by recreating them in <a href=\"/wiki/4K_resolution\" title=\"4K resolution\">4k</a> or higher resolutions via image training, and then down-sampling them to fit the game's native resolution (with results resembling the <a href=\"/wiki/Supersampling\" title=\"Supersampling\">supersampling</a> method of <a href=\"/wiki/Spatial_anti-aliasing\" title=\"Spatial anti-aliasing\">anti-aliasing</a>).<sup class=\"reference\" id=\"cite_ref-19\"><a href=\"#cite_note-19\">[19]</a></sup> With proper training, GANs provide a clearer and sharper 2D texture image magnitudes higher in quality than the original, while fully retaining the original's level of details, colors, etc. Known examples of extensive GAN usage include <a href=\"/wiki/Final_Fantasy_VIII\" title=\"Final Fantasy VIII\">Final Fantasy VIII</a>, <a href=\"/wiki/Final_Fantasy_IX\" title=\"Final Fantasy IX\">Final Fantasy IX</a>, <a href=\"/wiki/Resident_Evil_(2002_video_game)\" title=\"Resident Evil (2002 video game)\">Resident Evil</a> REmake HD Remaster, and <a href=\"/wiki/Max_Payne\" title=\"Max Payne\">Max Payne</a>.\n",
       "</p>\n",
       "<h3><span class=\"mw-headline\" id=\"Concerns_about_malicious_applications\">Concerns about malicious applications</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=6\" title=\"Edit section: Concerns about malicious applications\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h3>\n",
       "<p>Concerns have been raised about the potential use of GAN-based <a href=\"/wiki/Human_image_synthesis\" title=\"Human image synthesis\">human image synthesis</a> for sinister purposes, e.g., to produce fake and/or incriminating photographs and videos.<sup class=\"reference\" id=\"cite_ref-TPDNEwuaitcryhf|_20-0\"><a href=\"#cite_note-TPDNEwuaitcryhf|-20\">[20]</a></sup>\n",
       "GANs can be used to generate unique, realistic profile photos of people who do not exist, in order to automate creation of fake social media profiles.<sup class=\"reference\" id=\"cite_ref-21\"><a href=\"#cite_note-21\">[21]</a></sup>\n",
       "As of May 2019, the state of California is considering a bill that would ban the use of GANs and related technologies to make fake pornography without the consent of the people depicted.<sup class=\"reference\" id=\"cite_ref-22\"><a href=\"#cite_note-22\">[22]</a></sup> DARPA's Media Forensics program studies ways to counteract fake media, including fake media produced using GANs.<sup class=\"reference\" id=\"cite_ref-23\"><a href=\"#cite_note-23\">[23]</a></sup>\n",
       "</p>\n",
       "<h3><span class=\"mw-headline\" id=\"Miscellaneous_applications\">Miscellaneous applications</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=7\" title=\"Edit section: Miscellaneous applications\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h3>\n",
       "<p>GANs that produce <a class=\"mw-redirect\" href=\"/wiki/Photorealistic_rendering\" title=\"Photorealistic rendering\">photorealistic</a> images can be used to visualize <a href=\"/wiki/Interior_design\" title=\"Interior design\">interior design</a>, <a href=\"/wiki/Industrial_design\" title=\"Industrial design\">industrial design</a>, shoes, bags, and <a href=\"/wiki/Clothing\" title=\"Clothing\">clothing</a> items or items for <a href=\"/wiki/PC_game\" title=\"PC game\">computer games</a>' scenes.<sup class=\"noprint Inline-Template Template-Fact\" style=\"white-space:nowrap;\">[<i><a href=\"/wiki/Wikipedia:Citation_needed\" title=\"Wikipedia:Citation needed\"><span title=\"No source provided. How does a GAN do this? (February 2018)\">citation needed</span></a></i>]</sup> Such networks were reported to be used by <a href=\"/wiki/Facebook\" title=\"Facebook\">Facebook</a>.<sup class=\"reference\" id=\"cite_ref-24\"><a href=\"#cite_note-24\">[24]</a></sup>\n",
       "</p><p>GANs can <a href=\"/wiki/3D_reconstruction_from_multiple_images\" title=\"3D reconstruction from multiple images\">reconstruct 3D models of objects from images</a>,<sup class=\"reference\" id=\"cite_ref-25\"><a href=\"#cite_note-25\">[25]</a></sup> and model patterns of motion in video,<sup class=\"reference\" id=\"cite_ref-26\"><a href=\"#cite_note-26\">[26]</a></sup>\n",
       "</p><p>GANs can be used to age face photographs to show how an individual's appearance might change with age.<sup class=\"reference\" id=\"cite_ref-27\"><a href=\"#cite_note-27\">[27]</a></sup>\n",
       "</p><p>GANs can also be used to transfer map styles in Cartography.<sup class=\"reference\" id=\"cite_ref-28\"><a href=\"#cite_note-28\">[28]</a></sup>\n",
       "</p><p>StyleGAN itself is currently the sixth most trending <a href=\"/wiki/Python_(programming_language)\" title=\"Python (programming language)\">Python</a> project on <a href=\"/wiki/GitHub\" title=\"GitHub\">GitHub</a>.<sup class=\"reference\" id=\"cite_ref-29\"><a href=\"#cite_note-29\">[29]</a></sup>\n",
       "</p><p>A variation of the GANs are used in training a network to generate optimal control inputs to nonlinear dynamical systems. Where the discriminatory network is known as a critic that checks the optimality of the solution and the generative network is known as an Adaptive network that generates the optimal control. The critic and adaptive network train each other to approximate a nonlinear optimal control.<sup class=\"reference\" id=\"cite_ref-30\"><a href=\"#cite_note-30\">[30]</a></sup>\n",
       "</p><p>GANs have been used to visualize the effect that climate change will have on specific houses.<sup class=\"reference\" id=\"cite_ref-31\"><a href=\"#cite_note-31\">[31]</a></sup>\n",
       "</p><p>A GAN model called Speech2Face can reconstruct an image of a person's face after listening to their voice.<sup class=\"reference\" id=\"cite_ref-32\"><a href=\"#cite_note-32\">[32]</a></sup>\n",
       "</p>\n",
       "<h2><span class=\"mw-headline\" id=\"History\">History</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=8\" title=\"Edit section: History\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h2>\n",
       "<p><a href=\"/wiki/Adversarial_machine_learning\" title=\"Adversarial machine learning\">Adversarial machine learning</a> has other uses besides generative modeling and can be applied to models other than neural networks. The general idea of learning via competition between players dates back to at least 1959 with the influential work of <a href=\"/wiki/Arthur_Samuel\" title=\"Arthur Samuel\">Arthur Samuel</a>, demonstrating that algorithms could learn to play checkers via adversarial self-play.<sup class=\"reference\" id=\"cite_ref-33\"><a href=\"#cite_note-33\">[33]</a></sup>\n",
       "</p><p><a href=\"/wiki/Ian_Goodfellow\" title=\"Ian Goodfellow\">Ian Goodfellow</a> is recognized by several sources as having invented GANs<sup class=\"reference\" id=\"cite_ref-34\"><a href=\"#cite_note-34\">[34]</a></sup><sup class=\"reference\" id=\"cite_ref-35\"><a href=\"#cite_note-35\">[35]</a></sup><sup class=\"reference\" id=\"cite_ref-36\"><a href=\"#cite_note-36\">[36]</a></sup> in 2014.<sup class=\"reference\" id=\"cite_ref-GANnips_1-3\"><a href=\"#cite_note-GANnips-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-37\"><a href=\"#cite_note-37\">[37]</a></sup><sup class=\"reference\" id=\"cite_ref-38\"><a href=\"#cite_note-38\">[38]</a></sup>This paper included the first working implementation of a generative model based on adversarial networks, as well as game theoretic analysis establishing that the method is sound. However, there are several independent debates<sup class=\"reference\" id=\"cite_ref-reddit3_39-0\"><a href=\"#cite_note-reddit3-39\">[39]</a></sup><sup class=\"reference\" id=\"cite_ref-reddit5_40-0\"><a href=\"#cite_note-reddit5-40\">[40]</a></sup> over who invented which aspects of GANs.\n",
       "</p><p>Some review papers about GANs from academic sources, such as<sup class=\"reference\" id=\"cite_ref-41\"><a href=\"#cite_note-41\">[41]</a></sup> assign credit for the idea to <a href=\"/wiki/Ian_Goodfellow\" title=\"Ian Goodfellow\">Ian Goodfellow</a> and make no mention of <a class=\"mw-redirect\" href=\"/wiki/Juergen_Schmidhuber\" title=\"Juergen Schmidhuber\">Juergen Schmidhuber</a>. <a href=\"/wiki/Ian_Goodfellow\" title=\"Ian Goodfellow\">Ian Goodfellow</a>'s own peer-reviewed GAN paper<sup class=\"reference\" id=\"cite_ref-GANnips_1-4\"><a href=\"#cite_note-GANnips-1\">[1]</a></sup> mentions Schmidhuber's unsupervised adversarial technique called <i>predictability minimization</i> (PM), claiming that PM is not a minimax game. Schmidhuber disputes this claim,<sup class=\"reference\" id=\"cite_ref-sch2019_42-0\"><a href=\"#cite_note-sch2019-42\">[42]</a></sup> citing Equation 2 of the 1996 paper on PM.<sup class=\"reference\" id=\"cite_ref-sch1996pm_43-0\"><a href=\"#cite_note-sch1996pm-43\">[43]</a></sup> He also formulates<sup class=\"reference\" id=\"cite_ref-sch2019_42-1\"><a href=\"#cite_note-sch2019-42\">[42]</a></sup> GANs as special cases of his <i>Adversarial Curiosity</i> (1990).<sup class=\"reference\" id=\"cite_ref-sch1990curiosity_44-0\"><a href=\"#cite_note-sch1990curiosity-44\">[44]</a></sup><sup class=\"reference\" id=\"cite_ref-sch1991curiosity_45-0\"><a href=\"#cite_note-sch1991curiosity-45\">[45]</a></sup>\n",
       "Schmidhuber interrupted<sup class=\"reference\" id=\"cite_ref-bloomberg2018_46-0\"><a href=\"#cite_note-bloomberg2018-46\">[46]</a></sup> a presentation by Goodfellow in 2016 demanding more credit for his earlier work. Goodfellow himself states<sup class=\"reference\" id=\"cite_ref-47\"><a href=\"#cite_note-47\">[47]</a></sup> that the most direct inspiration for GANs was noise-contrastive estimation,<sup class=\"reference\" id=\"cite_ref-48\"><a href=\"#cite_note-48\">[48]</a></sup> which uses the same loss function as GANs and which Goodfellow studied during his PhD in 2010-2014.\n",
       "</p><p>Other people had similar ideas but did not develop them similarly. An idea involving adversarial networks was published in a 2010 blog post by Olli Niemitalo.<sup class=\"reference\" id=\"cite_ref-olli2010_49-0\"><a href=\"#cite_note-olli2010-49\">[49]</a></sup> This idea was never implemented and did not involve stochasticity in the generator and thus was not a generative model. It is now known as a conditional GAN or cGAN.<sup class=\"reference\" id=\"cite_ref-reddit3_39-1\"><a href=\"#cite_note-reddit3-39\">[39]</a></sup> An idea similar to GANs was used to model animal behavior by Li, Gauci and Gross in 2013.<sup class=\"reference\" id=\"cite_ref-Li-etal-GECCO2013_50-0\"><a href=\"#cite_note-Li-etal-GECCO2013-50\">[50]</a></sup>\n",
       "</p><p>In 2017, a GAN was used for image enhancement focusing on realistic textures rather than pixel-accuracy, producing a higher image quality at high magnification.<sup class=\"reference\" id=\"cite_ref-51\"><a href=\"#cite_note-51\">[51]</a></sup> In 2017, the first faces were generated.<sup class=\"reference\" id=\"cite_ref-52\"><a href=\"#cite_note-52\">[52]</a></sup> These were exhibited in February 2018 at the Grand Palais.<sup class=\"reference\" id=\"cite_ref-53\"><a href=\"#cite_note-53\">[53]</a></sup><sup class=\"reference\" id=\"cite_ref-54\"><a href=\"#cite_note-54\">[54]</a></sup> Faces generated by <a href=\"/wiki/StyleGAN\" title=\"StyleGAN\">StyleGAN</a><sup class=\"reference\" id=\"cite_ref-55\"><a href=\"#cite_note-55\">[55]</a></sup> in 2019 drew comparisons with <a href=\"/wiki/Deepfake\" title=\"Deepfake\">deepfakes</a>.<sup class=\"reference\" id=\"cite_ref-TPDNEitboowo2019_56-0\"><a href=\"#cite_note-TPDNEitboowo2019-56\">[56]</a></sup><sup class=\"reference\" id=\"cite_ref-TPDNE_57-0\"><a href=\"#cite_note-TPDNE-57\">[57]</a></sup><sup class=\"reference\" id=\"cite_ref-Style-based_GANs_–_Generating_and_Tuning_Realistic_Artificial_Faces_58-0\"><a href=\"#cite_note-Style-based_GANs_–_Generating_and_Tuning_Realistic_Artificial_Faces-58\">[58]</a></sup>\n",
       "</p><p>Beginning in 2017, GAN technology began to make its presence felt in the fine arts arena with the appearance of a newly developed implementation which was said to have crossed the threshold of being able to generate unique and appealing abstract paintings, and thus dubbed a \"CAN\", for \"creative adversarial network\".<sup class=\"reference\" id=\"cite_ref-59\"><a href=\"#cite_note-59\">[59]</a></sup> A GAN system was used to create the 2018 painting <i><a href=\"/wiki/Edmond_de_Belamy\" title=\"Edmond de Belamy\">Edmond de Belamy</a>,</i> which sold for US$432,500.<sup class=\"reference\" id=\"cite_ref-60\"><a href=\"#cite_note-60\">[60]</a></sup> An early 2019 article by members of the original CAN team discussed further progress with that system, and gave consideration as well to the overall prospects for an AI-enabled art.<sup class=\"reference\" id=\"cite_ref-61\"><a href=\"#cite_note-61\">[61]</a></sup>\n",
       "</p><p>In May 2019, researchers at Samsung demonstrated a GAN-based system that produces videos of a person speaking given only a single photo of that person.<sup class=\"reference\" id=\"cite_ref-62\"><a href=\"#cite_note-62\">[62]</a></sup>\n",
       "</p>\n",
       "<h2><span class=\"mw-headline\" id=\"References\">References</span><span class=\"mw-editsection\"><span class=\"mw-editsection-bracket\">[</span><a href=\"/w/index.php?title=Generative_adversarial_network&amp;action=edit&amp;section=9\" title=\"Edit section: References\">edit</a><span class=\"mw-editsection-bracket\">]</span></span></h2>\n",
       "<div class=\"reflist columns references-column-width\" style=\"-moz-column-width: 30em; -webkit-column-width: 30em; column-width: 30em; list-style-type: decimal;\">\n",
       "<ol class=\"references\">\n",
       "<li id=\"cite_note-GANnips-1\"><span class=\"mw-cite-backlink\">^ <a href=\"#cite_ref-GANnips_1-0\"><sup><i><b>a</b></i></sup></a> <a href=\"#cite_ref-GANnips_1-1\"><sup><i><b>b</b></i></sup></a> <a href=\"#cite_ref-GANnips_1-2\"><sup><i><b>c</b></i></sup></a> <a href=\"#cite_ref-GANnips_1-3\"><sup><i><b>d</b></i></sup></a> <a href=\"#cite_ref-GANnips_1-4\"><sup><i><b>e</b></i></sup></a></span> <span class=\"reference-text\"><cite class=\"citation conference\">Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). <a class=\"external text\" href=\"https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf\" rel=\"nofollow\"><i>Generative Adversarial Networks</i></a> <span class=\"cs1-format\">(PDF)</span>. Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=conference&amp;rft.btitle=Generative+Adversarial+Networks&amp;rft.pages=2672-2680&amp;rft.date=2014&amp;rft.aulast=Goodfellow&amp;rft.aufirst=Ian&amp;rft.au=Pouget-Abadie%2C+Jean&amp;rft.au=Mirza%2C+Mehdi&amp;rft.au=Xu%2C+Bing&amp;rft.au=Warde-Farley%2C+David&amp;rft.au=Ozair%2C+Sherjil&amp;rft.au=Courville%2C+Aaron&amp;rft.au=Bengio%2C+Yoshua&amp;rft_id=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5423-generative-adversarial-nets.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><style data-mw-deduplicate=\"TemplateStyles:r886058088\">.mw-parser-output cite.citation{font-style:inherit}.mw-parser-output .citation q{quotes:\"\\\"\"\"\\\"\"\"'\"\"'\"}.mw-parser-output .citation .cs1-lock-free a{background:url(\"//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png\")no-repeat;background-position:right .1em center}.mw-parser-output .citation .cs1-lock-limited a,.mw-parser-output .citation .cs1-lock-registration a{background:url(\"//upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Lock-gray-alt-2.svg/9px-Lock-gray-alt-2.svg.png\")no-repeat;background-position:right .1em center}.mw-parser-output .citation .cs1-lock-subscription a{background:url(\"//upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Lock-red-alt-2.svg/9px-Lock-red-alt-2.svg.png\")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration{color:#555}.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration span{border-bottom:1px dotted;cursor:help}.mw-parser-output .cs1-ws-icon a{background:url(\"//upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Wikisource-logo.svg/12px-Wikisource-logo.svg.png\")no-repeat;background-position:right .1em center}.mw-parser-output code.cs1-code{color:inherit;background:inherit;border:inherit;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;font-size:100%}.mw-parser-output .cs1-visible-error{font-size:100%}.mw-parser-output .cs1-maint{display:none;color:#33aa33;margin-left:0.3em}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-right{padding-right:0.2em}</style></span>\n",
       "</li>\n",
       "<li id=\"cite_note-ITT_GANs-2\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-ITT_GANs_2-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation arxiv\">Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi (2016). \"Improved Techniques for Training GANs\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1606.03498\" rel=\"nofollow\">1606.03498</a></span> [<a class=\"external text\" href=\"//arxiv.org/archive/cs.LG\" rel=\"nofollow\">cs.LG</a>].</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=preprint&amp;rft.jtitle=arXiv&amp;rft.atitle=Improved+Techniques+for+Training+GANs&amp;rft.date=2016&amp;rft_id=info%3Aarxiv%2F1606.03498&amp;rft.aulast=Salimans&amp;rft.aufirst=Tim&amp;rft.au=Goodfellow%2C+Ian&amp;rft.au=Zaremba%2C+Wojciech&amp;rft.au=Cheung%2C+Vicki&amp;rft.au=Radford%2C+Alec&amp;rft.au=Chen%2C+Xi&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
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       "<li id=\"cite_note-5\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-5\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">LeCun, Yann. <a class=\"external text\" href=\"https://www.youtube.com/watch?v=IbjF5VjniVE\" rel=\"nofollow\">\"RL Seminar: The Next Frontier in AI: Unsupervised Learning\"</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=RL+Seminar%3A+The+Next+Frontier+in+AI%3A+Unsupervised+Learning&amp;rft.aulast=LeCun&amp;rft.aufirst=Yann&amp;rft_id=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DIbjF5VjniVE&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
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       "<li id=\"cite_note-6\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-6\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Luc, Pauline; Couprie, Camille; Chintala, Soumith; Verbeek, Jakob (2016-11-25). \"Semantic Segmentation using Adversarial Networks\". <i>NIPS Workshop on Adversarial Training, Dec, Barcelona, Spain</i>. <b>2016</b>. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1611.08408\" rel=\"nofollow\">1611.08408</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2016arXiv161108408L\" rel=\"nofollow\">2016arXiv161108408L</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=NIPS+Workshop+on+Adversarial+Training%2C+Dec%2C+Barcelona%2C+Spain&amp;rft.atitle=Semantic+Segmentation+using+Adversarial+Networks&amp;rft.volume=2016&amp;rft.date=2016-11-25&amp;rft_id=info%3Aarxiv%2F1611.08408&amp;rft_id=info%3Abibcode%2F2016arXiv161108408L&amp;rft.aulast=Luc&amp;rft.aufirst=Pauline&amp;rft.au=Couprie%2C+Camille&amp;rft.au=Chintala%2C+Soumith&amp;rft.au=Verbeek%2C+Jakob&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
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       "<li id=\"cite_note-10\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-10\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Dietmar, Julia. <a class=\"external text\" href=\"https://www.forbes.com/sites/forbestechcouncil/2019/05/21/gans-and-deepfakes-could-revolutionize-the-fashion-industry/#515624593d17\" rel=\"nofollow\">\"GANs and Deepfakes Could Revolutionize The Fashion Industry\"</a>. <i>Forbes</i>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Forbes&amp;rft.atitle=GANs+and+Deepfakes+Could+Revolutionize+The+Fashion+Industry&amp;rft.aulast=Dietmar&amp;rft.aufirst=Julia&amp;rft_id=https%3A%2F%2Fwww.forbes.com%2Fsites%2Fforbestechcouncil%2F2019%2F05%2F21%2Fgans-and-deepfakes-could-revolutionize-the-fashion-industry%2F%23515624593d17&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-11\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-11\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan (2017-02-01). \"Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit\". <i>Monthly Notices of the Royal Astronomical Society: Letters</i>. <b>467</b> (1): L110–L114. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1702.00403\" rel=\"nofollow\">1702.00403</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2017MNRAS.467L.110S\" rel=\"nofollow\">2017MNRAS.467L.110S</a>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1093%2Fmnrasl%2Fslx008\" rel=\"nofollow\">10.1093/mnrasl/slx008</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Monthly+Notices+of+the+Royal+Astronomical+Society%3A+Letters&amp;rft.atitle=Generative+Adversarial+Networks+recover+features+in+astrophysical+images+of+galaxies+beyond+the+deconvolution+limit&amp;rft.volume=467&amp;rft.issue=1&amp;rft.pages=L110-L114&amp;rft.date=2017-02-01&amp;rft_id=info%3Aarxiv%2F1702.00403&amp;rft_id=info%3Adoi%2F10.1093%2Fmnrasl%2Fslx008&amp;rft_id=info%3Abibcode%2F2017MNRAS.467L.110S&amp;rft.aulast=Schawinski&amp;rft.aufirst=Kevin&amp;rft.au=Zhang%2C+Ce&amp;rft.au=Zhang%2C+Hantian&amp;rft.au=Fowler%2C+Lucas&amp;rft.au=Santhanam%2C+Gokula+Krishnan&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-12\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-12\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Kincade, Kathy. <a class=\"external text\" href=\"https://www.rdmag.com/news/2019/05/researchers-train-neural-network-study-dark-matter\" rel=\"nofollow\">\"Researchers Train a Neural Network to Study Dark Matter\"</a>. R&amp;D Magazine.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=Researchers+Train+a+Neural+Network+to+Study+Dark+Matter&amp;rft.aulast=Kincade&amp;rft.aufirst=Kathy&amp;rft_id=https%3A%2F%2Fwww.rdmag.com%2Fnews%2F2019%2F05%2Fresearchers-train-neural-network-study-dark-matter&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-13\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-13\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Kincade, Kathy (May 16, 2019). <a class=\"external text\" href=\"https://phys.org/news/2019-05-cosmogan-neural-network-dark.html\" rel=\"nofollow\">\"CosmoGAN: Training a neural network to study dark matter\"</a>. Phys.org.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=CosmoGAN%3A+Training+a+neural+network+to+study+dark+matter&amp;rft.date=2019-05-16&amp;rft.aulast=Kincade&amp;rft.aufirst=Kathy&amp;rft_id=https%3A%2F%2Fphys.org%2Fnews%2F2019-05-cosmogan-neural-network-dark.html&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-14\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-14\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\"><a class=\"external text\" href=\"https://www.sciencedaily.com/releases/2019/05/190516145206.htm\" rel=\"nofollow\">\"Training a neural network to study dark matter\"</a>. Science Daily. May 16, 2019.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=Training+a+neural+network+to+study+dark+matter&amp;rft.date=2019-05-16&amp;rft_id=https%3A%2F%2Fwww.sciencedaily.com%2Freleases%2F2019%2F05%2F190516145206.htm&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-15\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-15\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">at 06:13, Katyanna Quach 20 May 2019. <a class=\"external text\" href=\"https://www.theregister.co.uk/2019/05/20/neural_networks_dark_matter/\" rel=\"nofollow\">\"Cosmoboffins use neural networks to build dark matter maps the easy way\"</a>. <i>www.theregister.co.uk</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-05-20</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=www.theregister.co.uk&amp;rft.atitle=Cosmoboffins+use+neural+networks+to+build+dark+matter+maps+the+easy+way&amp;rft.aulast=at+06%3A13&amp;rft.aufirst=Katyanna+Quach+20+May+2019&amp;rft_id=https%3A%2F%2Fwww.theregister.co.uk%2F2019%2F05%2F20%2Fneural_networks_dark_matter%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-16\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-16\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Mustafa, Mustafa; Bard, Deborah; Bhimji, Wahid; Lukić, Zarija; Al-Rfou, Rami; Kratochvil, Jan M. (2019-05-06). \"CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks\". <i>Computational Astrophysics and Cosmology</i>. <b>6</b> (1): 1. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1706.02390\" rel=\"nofollow\">1706.02390</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2019ComAC...6....1M\" rel=\"nofollow\">2019ComAC...6....1M</a>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1186%2Fs40668-019-0029-9\" rel=\"nofollow\">10.1186/s40668-019-0029-9</a>. <a href=\"/wiki/International_Standard_Serial_Number\" title=\"International Standard Serial Number\">ISSN</a> <a class=\"external text\" href=\"//www.worldcat.org/issn/2197-7909\" rel=\"nofollow\">2197-7909</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Computational+Astrophysics+and+Cosmology&amp;rft.atitle=CosmoGAN%3A+creating+high-fidelity+weak+lensing+convergence+maps+using+Generative+Adversarial+Networks&amp;rft.volume=6&amp;rft.issue=1&amp;rft.pages=1&amp;rft.date=2019-05-06&amp;rft_id=info%3Aarxiv%2F1706.02390&amp;rft.issn=2197-7909&amp;rft_id=info%3Adoi%2F10.1186%2Fs40668-019-0029-9&amp;rft_id=info%3Abibcode%2F2019ComAC...6....1M&amp;rft.aulast=Mustafa&amp;rft.aufirst=Mustafa&amp;rft.au=Bard%2C+Deborah&amp;rft.au=Bhimji%2C+Wahid&amp;rft.au=Luki%C4%87%2C+Zarija&amp;rft.au=Al-Rfou%2C+Rami&amp;rft.au=Kratochvil%2C+Jan+M.&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-17\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-17\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Erdmann, Martin; Glombitza, Jonas; Quast, Thorben (2019). \"Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network\". <i>Computing and Software for Big Science</i>. <b>3</b>: 4. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1807.01954\" rel=\"nofollow\">1807.01954</a></span>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1007%2Fs41781-018-0019-7\" rel=\"nofollow\">10.1007/s41781-018-0019-7</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Computing+and+Software+for+Big+Science&amp;rft.atitle=Precise+Simulation+of+Electromagnetic+Calorimeter+Showers+Using+a+Wasserstein+Generative+Adversarial+Network&amp;rft.volume=3&amp;rft.pages=4&amp;rft.date=2019&amp;rft_id=info%3Aarxiv%2F1807.01954&amp;rft_id=info%3Adoi%2F10.1007%2Fs41781-018-0019-7&amp;rft.aulast=Erdmann&amp;rft.aufirst=Martin&amp;rft.au=Glombitza%2C+Jonas&amp;rft.au=Quast%2C+Thorben&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-18\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-18\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Musella, Pasquale; Pandolfi, Francesco (2018). \"Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks\". <i>Computing and Software for Big Science</i>. <b>2</b>: 8. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1805.00850\" rel=\"nofollow\">1805.00850</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2018arXiv180500850M\" rel=\"nofollow\">2018arXiv180500850M</a>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1007%2Fs41781-018-0015-y\" rel=\"nofollow\">10.1007/s41781-018-0015-y</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Computing+and+Software+for+Big+Science&amp;rft.atitle=Fast+and+Accurate+Simulation+of+Particle+Detectors+Using+Generative+Adversarial+Networks&amp;rft.volume=2&amp;rft.pages=8&amp;rft.date=2018&amp;rft_id=info%3Aarxiv%2F1805.00850&amp;rft_id=info%3Adoi%2F10.1007%2Fs41781-018-0015-y&amp;rft_id=info%3Abibcode%2F2018arXiv180500850M&amp;rft.aulast=Musella&amp;rft.aufirst=Pasquale&amp;rft.au=Pandolfi%2C+Francesco&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-19\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-19\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Tang, Xiaoou; Qiao, Yu; Loy, Chen Change; Dong, Chao; Liu, Yihao; Gu, Jinjin; Wu, Shixiang; Yu, Ke; Wang, Xintao (2018-09-01). \"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1809.00219v2\" rel=\"nofollow\">1809.00219v2</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2018arXiv180900219W\" rel=\"nofollow\">2018arXiv180900219W</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=ESRGAN%3A+Enhanced+Super-Resolution+Generative+Adversarial+Networks&amp;rft.date=2018-09-01&amp;rft_id=info%3Aarxiv%2F1809.00219v2&amp;rft_id=info%3Abibcode%2F2018arXiv180900219W&amp;rft.aulast=Tang&amp;rft.aufirst=Xiaoou&amp;rft.au=Qiao%2C+Yu&amp;rft.au=Loy%2C+Chen+Change&amp;rft.au=Dong%2C+Chao&amp;rft.au=Liu%2C+Yihao&amp;rft.au=Gu%2C+Jinjin&amp;rft.au=Wu%2C+Shixiang&amp;rft.au=Yu%2C+Ke&amp;rft.au=Wang%2C+Xintao&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-TPDNEwuaitcryhf|-20\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-TPDNEwuaitcryhf|_20-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">msmash (2019-02-14). <a class=\"external text\" href=\"https://tech.slashdot.org/story/19/02/14/199200/this-person-does-not-exist-website-uses-ai-to-create-realistic-yet-horrifying-faces\" rel=\"nofollow\">\"<span class=\"cs1-kern-left\">'</span>This Person Does Not Exist' Website Uses AI To Create Realistic Yet Horrifying Faces\"</a>. <i>Slashdot</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-02-16</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Slashdot&amp;rft.atitle=%27This+Person+Does+Not+Exist%27+Website+Uses+AI+To+Create+Realistic+Yet+Horrifying+Faces&amp;rft.date=2019-02-14&amp;rft.au=msmash&amp;rft_id=https%3A%2F%2Ftech.slashdot.org%2Fstory%2F19%2F02%2F14%2F199200%2Fthis-person-does-not-exist-website-uses-ai-to-create-realistic-yet-horrifying-faces&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-21\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-21\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Doyle, Michael (May 16, 2019). <a class=\"external text\" href=\"https://www.courierpress.com/story/news/crime/2019/05/16/john-beasley-lives-saddlehorse-drive-evansville-does-he/3700111002/\" rel=\"nofollow\">\"John Beasley lives on Saddlehorse Drive in Evansville. Or does he?\"</a>. Courier and Press.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=John+Beasley+lives+on+Saddlehorse+Drive+in+Evansville.+Or+does+he%3F&amp;rft.date=2019-05-16&amp;rft.aulast=Doyle&amp;rft.aufirst=Michael&amp;rft_id=https%3A%2F%2Fwww.courierpress.com%2Fstory%2Fnews%2Fcrime%2F2019%2F05%2F16%2Fjohn-beasley-lives-saddlehorse-drive-evansville-does-he%2F3700111002%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-22\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-22\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Targett, Ed (May 16, 2019). \"California moves closer to making deepfake pornography illegal\". Computer Business Review.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=California+moves+closer+to+making+deepfake+pornography+illegal&amp;rft.date=2019-05-16&amp;rft.aulast=Targett&amp;rft.aufirst=Ed&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-23\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-23\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Knight, Will (Aug 7, 2018). <a class=\"external text\" href=\"https://www.technologyreview.com/s/611726/the-defense-department-has-produced-the-first-tools-for-catching-deepfakes/\" rel=\"nofollow\">\"The Defense Department has produced the first tools for catching deepfakes\"</a>. MIT Technology Review.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=The+Defense+Department+has+produced+the+first+tools+for+catching+deepfakes&amp;rft.date=2018-08-07&amp;rft.aulast=Knight&amp;rft.aufirst=Will&amp;rft_id=https%3A%2F%2Fwww.technologyreview.com%2Fs%2F611726%2Fthe-defense-department-has-produced-the-first-tools-for-catching-deepfakes%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-24\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-24\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Greenemeier, Larry (June 20, 2016). <a class=\"external text\" href=\"https://www.scientificamerican.com/article/when-will-computers-have-common-sense-ask-facebook/\" rel=\"nofollow\">\"When Will Computers Have Common Sense? Ask Facebook\"</a>. <i>Scientific American</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">July 31,</span> 2016</span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Scientific+American&amp;rft.atitle=When+Will+Computers+Have+Common+Sense%3F+Ask+Facebook&amp;rft.date=2016-06-20&amp;rft.aulast=Greenemeier&amp;rft.aufirst=Larry&amp;rft_id=https%3A%2F%2Fwww.scientificamerican.com%2Farticle%2Fwhen-will-computers-have-common-sense-ask-facebook%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-25\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-25\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"http://3dgan.csail.mit.edu/\" rel=\"nofollow\">\"3D Generative Adversarial Network\"</a>. <i>3dgan.csail.mit.edu</i>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=3dgan.csail.mit.edu&amp;rft.atitle=3D+Generative+Adversarial+Network&amp;rft_id=http%3A%2F%2F3dgan.csail.mit.edu%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-26\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-26\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Vondrick, Carl; Pirsiavash, Hamed; Torralba, Antonio (2016). <a class=\"external text\" href=\"http://www.cs.columbia.edu/~vondrick/tinyvideo/\" rel=\"nofollow\">\"Generating Videos with Scene Dynamics\"</a>. <i>carlvondrick.com</i>. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1609.02612\" rel=\"nofollow\">1609.02612</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2016arXiv160902612V\" rel=\"nofollow\">2016arXiv160902612V</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=carlvondrick.com&amp;rft.atitle=Generating+Videos+with+Scene+Dynamics&amp;rft.date=2016&amp;rft_id=info%3Aarxiv%2F1609.02612&amp;rft_id=info%3Abibcode%2F2016arXiv160902612V&amp;rft.aulast=Vondrick&amp;rft.aufirst=Carl&amp;rft.au=Pirsiavash%2C+Hamed&amp;rft.au=Torralba%2C+Antonio&amp;rft_id=http%3A%2F%2Fwww.cs.columbia.edu%2F~vondrick%2Ftinyvideo%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-27\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-27\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation arxiv\">Antipov, Grigory; Baccouche, Moez; Dugelay, Jean-Luc (2017). \"Face Aging With Conditional Generative Adversarial Networks\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1702.01983\" rel=\"nofollow\">1702.01983</a></span> [<a class=\"external text\" href=\"//arxiv.org/archive/cs.CV\" rel=\"nofollow\">cs.CV</a>].</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=preprint&amp;rft.jtitle=arXiv&amp;rft.atitle=Face+Aging+With+Conditional+Generative+Adversarial+Networks&amp;rft.date=2017&amp;rft_id=info%3Aarxiv%2F1702.01983&amp;rft.aulast=Antipov&amp;rft.aufirst=Grigory&amp;rft.au=Baccouche%2C+Moez&amp;rft.au=Dugelay%2C+Jean-Luc&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-28\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-28\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Kang, Yuhao; Gao, Song; Roth, Rob (2019). <a class=\"external text\" href=\"https://geods.geography.wisc.edu/archives/1192\" rel=\"nofollow\">\"Transferring Multiscale Map Styles Using Generative Adversarial Networks\"</a>. <i>International Journal of Cartography</i>. <b>5</b> (2–3): 115–141. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1905.02200\" rel=\"nofollow\">1905.02200</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2019arXiv190502200K\" rel=\"nofollow\">2019arXiv190502200K</a>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1080%2F23729333.2019.1615729\" rel=\"nofollow\">10.1080/23729333.2019.1615729</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=International+Journal+of+Cartography&amp;rft.atitle=Transferring+Multiscale+Map+Styles+Using+Generative+Adversarial+Networks&amp;rft.volume=5&amp;rft.issue=2%E2%80%933&amp;rft.pages=115-141&amp;rft.date=2019&amp;rft_id=info%3Aarxiv%2F1905.02200&amp;rft_id=info%3Adoi%2F10.1080%2F23729333.2019.1615729&amp;rft_id=info%3Abibcode%2F2019arXiv190502200K&amp;rft.aulast=Kang&amp;rft.aufirst=Yuhao&amp;rft.au=Gao%2C+Song&amp;rft.au=Roth%2C+Rob&amp;rft_id=https%3A%2F%2Fgeods.geography.wisc.edu%2Farchives%2F1192&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-29\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-29\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Roboticist, Javad Amirian (February 14, 2019). <a class=\"external text\" href=\"https://twitter.com/AmiryanJavad/status/1096126840434053120\" rel=\"nofollow\">\"#StyleGan, is ranked 6th among trending python projects on #Github. #DeepLearning #GenerativeNetworks\"</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=%23StyleGan%2C+is+ranked+6th+among+trending+python+projects+on+%23Github.+%23DeepLearning+%23GenerativeNetworks&amp;rft.date=2019-02-14&amp;rft.aulast=Roboticist&amp;rft.aufirst=Javad+Amirian&amp;rft_id=https%3A%2F%2Ftwitter.com%2FAmiryanJavad%2Fstatus%2F1096126840434053120&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-30\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-30\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Padhi, Radhakant; Unnikrishnan, Nishant (2006). \"A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems\". <i>Neural Networks</i>. <b>19</b> (10): 1648–1660. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1016%2Fj.neunet.2006.08.010\" rel=\"nofollow\">10.1016/j.neunet.2006.08.010</a>. <a class=\"mw-redirect\" href=\"/wiki/PubMed_Identifier\" title=\"PubMed Identifier\">PMID</a> <a class=\"external text\" href=\"//www.ncbi.nlm.nih.gov/pubmed/17045458\" rel=\"nofollow\">17045458</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Neural+Networks&amp;rft.atitle=A+single+network+adaptive+critic+%28SNAC%29+architecture+for+optimal+control+synthesis+for+a+class+of+nonlinear+systems&amp;rft.volume=19&amp;rft.issue=10&amp;rft.pages=1648-1660&amp;rft.date=2006&amp;rft_id=info%3Adoi%2F10.1016%2Fj.neunet.2006.08.010&amp;rft_id=info%3Apmid%2F17045458&amp;rft.aulast=Padhi&amp;rft.aufirst=Radhakant&amp;rft.au=Unnikrishnan%2C+Nishant&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-31\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-31\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\"><a class=\"external text\" href=\"https://www.technologyreview.com/f/613547/ai-can-show-us-the-ravages-of-climate-change/\" rel=\"nofollow\">\"AI can show us the ravages of climate change\"</a>. MIT Technology Review. May 16, 2019.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=AI+can+show+us+the+ravages+of+climate+change&amp;rft.date=2019-05-16&amp;rft_id=https%3A%2F%2Fwww.technologyreview.com%2Ff%2F613547%2Fai-can-show-us-the-ravages-of-climate-change%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-32\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-32\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Christian, Jon (May 28, 2019). <a class=\"external text\" href=\"https://futurism.com/the-byte/ai-guesses-appearance-voice\" rel=\"nofollow\">\"ASTOUNDING AI GUESSES WHAT YOU LOOK LIKE BASED ON YOUR VOICE\"</a>. Futurism.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=ASTOUNDING+AI+GUESSES+WHAT+YOU+LOOK+LIKE+BASED+ON+YOUR+VOICE&amp;rft.date=2019-05-28&amp;rft.aulast=Christian&amp;rft.aufirst=Jon&amp;rft_id=https%3A%2F%2Ffuturism.com%2Fthe-byte%2Fai-guesses-appearance-voice&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-33\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-33\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Samuel, Arthur (July 1959). <a class=\"external text\" href=\"https://ieeexplore.ieee.org/document/5392560\" rel=\"nofollow\">\"Some Studies in Machine Learning Using the Game of Checkers\"</a>. <i>IBM Journal of Research and Development</i>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=IBM+Journal+of+Research+and+Development&amp;rft.atitle=Some+Studies+in+Machine+Learning+Using+the+Game+of+Checkers&amp;rft.date=1959-07&amp;rft.aulast=Samuel&amp;rft.aufirst=Arthur&amp;rft_id=https%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F5392560&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-34\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-34\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Klok, Christie (February 21, 2018). <a class=\"external text\" href=\"https://www.technologyreview.com/s/610253/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/\" rel=\"nofollow\">\"The GANfather: The man who's given machines the gift of imagination\"</a>. MIT Technology Review<span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">14 May</span> 2019</span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=The+GANfather%3A+The+man+who%27s+given+machines+the+gift+of+imagination&amp;rft.date=2018-02-21&amp;rft.aulast=Klok&amp;rft.aufirst=Christie&amp;rft_id=https%3A%2F%2Fwww.technologyreview.com%2Fs%2F610253%2Fthe-ganfather-the-man-whos-given-machines-the-gift-of-imagination%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-35\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-35\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://www.innovatorsunder35.com/the-list/ian-goodfellow/\" rel=\"nofollow\">\"Invented a way for neural networks to get better by working together\"</a>. <i>Innovators under 35</i>. MIT Technology Review.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Innovators+under+35&amp;rft.atitle=Invented+a+way+for+neural+networks+to+get+better+by+working+together.&amp;rft_id=https%3A%2F%2Fwww.innovatorsunder35.com%2Fthe-list%2Fian-goodfellow%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-36\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-36\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Novet, Jordan (April 4, 2019). <a class=\"external text\" href=\"https://www.cnbc.com/2019/04/04/apple-hires-ai-expert-ian-goodfellow-from-google.html\" rel=\"nofollow\">\"One of Google's top A.I. people has joined Apple\"</a>. CNBC.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=One+of+Google%27s+top+A.I.+people+has+joined+Apple&amp;rft.date=2019-04-04&amp;rft.aulast=Novet&amp;rft.aufirst=Jordan&amp;rft_id=https%3A%2F%2Fwww.cnbc.com%2F2019%2F04%2F04%2Fapple-hires-ai-expert-ian-goodfellow-from-google.html&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-37\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-37\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Giles, Martin. <a class=\"external text\" href=\"https://www.technologyreview.com/s/610253/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/\" rel=\"nofollow\">\"The GANfather: The man who's given machines the gift of imagination\"</a>. <i>MIT Technology Review</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-06-21</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=MIT+Technology+Review&amp;rft.atitle=The+GANfather%3A+The+man+who%27s+given+machines+the+gift+of+imagination&amp;rft.aulast=Giles&amp;rft.aufirst=Martin&amp;rft_id=https%3A%2F%2Fwww.technologyreview.com%2Fs%2F610253%2Fthe-ganfather-the-man-whos-given-machines-the-gift-of-imagination%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-38\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-38\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation\" id=\"CITEREFSteven_Van_Vaerenbergh2018\">Steven Van Vaerenbergh (2018-01-18), <a class=\"external text\" href=\"https://www.youtube.com/watch?v=HGYYEUSm-0Q&amp;feature=youtu.be\" rel=\"nofollow\"><i>Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)</i></a><span class=\"reference-accessdate\">, retrieved <span class=\"nowrap\">2019-06-21</span></span></cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=book&amp;rft.btitle=Ian+Goodfellow%3A+Generative+Adversarial+Networks+%28NIPS+2016+tutorial%29&amp;rft.date=2018-01-18&amp;rft.au=Steven+Van+Vaerenbergh&amp;rft_id=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DHGYYEUSm-0Q%26feature%3Dyoutu.be&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-reddit3-39\"><span class=\"mw-cite-backlink\">^ <a href=\"#cite_ref-reddit3_39-0\"><sup><i><b>a</b></i></sup></a> <a href=\"#cite_ref-reddit3_39-1\"><sup><i><b>b</b></i></sup></a></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://www.reddit.com/r/MachineLearning/comments/bnqm0p/d_gans_were_invented_in_2010/\" rel=\"nofollow\">\"GANs were invented in 2010?\"</a>. <i>reddit r/MachineLearning</i>. 2019<span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-05-28</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=reddit+r%2FMachineLearning&amp;rft.atitle=GANs+were+invented+in+2010%3F&amp;rft.date=2019&amp;rft_id=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2Fbnqm0p%2Fd_gans_were_invented_in_2010%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-reddit5-40\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-reddit5_40-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://www.reddit.com/r/MachineLearning/comments/bzldq4/r_190604493_unsupervised_minimax_adversarial/\" rel=\"nofollow\">\"[R] [1906.04493] Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization (Schmidhuber)\"</a>. <i>reddit r/MachineLearning</i>. 2019<span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-06-12</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=reddit+r%2FMachineLearning&amp;rft.atitle=%5BR%5D+%5B1906.04493%5D+Unsupervised+Minimax%3A+Adversarial+Curiosity%2C+Generative+Adversarial+Networks%2C+and+Predictability+Minimization+%28Schmidhuber%29&amp;rft.date=2019&amp;rft_id=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2Fbzldq4%2Fr_190604493_unsupervised_minimax_adversarial%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-41\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-41\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Cresswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil (Jan 1, 2018). \"Generative Adversarial Networks: An Overview\". <i>IEEE Signal Processing</i>. <b>35</b> (1): 53–65. <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1710.07035\" rel=\"nofollow\">1710.07035</a></span>. <a href=\"/wiki/Bibcode\" title=\"Bibcode\">Bibcode</a>:<a class=\"external text\" href=\"http://adsabs.harvard.edu/abs/2018ISPM...35...53C\" rel=\"nofollow\">2018ISPM...35...53C</a>. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1109%2FMSP.2017.2765202\" rel=\"nofollow\">10.1109/MSP.2017.2765202</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=IEEE+Signal+Processing&amp;rft.atitle=Generative+Adversarial+Networks%3A+An+Overview&amp;rft.volume=35&amp;rft.issue=1&amp;rft.pages=53-65&amp;rft.date=2018-01-01&amp;rft_id=info%3Aarxiv%2F1710.07035&amp;rft_id=info%3Adoi%2F10.1109%2FMSP.2017.2765202&amp;rft_id=info%3Abibcode%2F2018ISPM...35...53C&amp;rft.aulast=Cresswell&amp;rft.aufirst=Antonia&amp;rft.au=White%2C+Tom&amp;rft.au=Dumoulin%2C+Vincent&amp;rft.au=Arulkumaran%2C+Kai&amp;rft.au=Sengupta%2C+Biswa&amp;rft.au=Bharath%2C+Anil&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-sch2019-42\"><span class=\"mw-cite-backlink\">^ <a href=\"#cite_ref-sch2019_42-0\"><sup><i><b>a</b></i></sup></a> <a href=\"#cite_ref-sch2019_42-1\"><sup><i><b>b</b></i></sup></a></span> <span class=\"reference-text\"><cite class=\"citation arxiv\">Schmidhuber, Juergen (2019). \"Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1906.04493\" rel=\"nofollow\">1906.04493</a></span> [<a class=\"external text\" href=\"//arxiv.org/archive/cs.NE\" rel=\"nofollow\">cs.NE</a>].</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=preprint&amp;rft.jtitle=arXiv&amp;rft.atitle=Unsupervised+Minimax%3A+Adversarial+Curiosity%2C+Generative+Adversarial+Networks%2C+and+Predictability+Minimization&amp;rft.date=2019&amp;rft_id=info%3Aarxiv%2F1906.04493&amp;rft.aulast=Schmidhuber&amp;rft.aufirst=Juergen&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-sch1996pm-43\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-sch1996pm_43-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Schmidhuber, Jürgen; Eldracher, Martin; Foltin, Bernhard (1996). \"Semilinear predictability minimzation produces well-known feature detectors\". <i>Neural Computation</i>. <b>8</b> (4): 773–786. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1162%2Fneco.1996.8.4.773\" rel=\"nofollow\">10.1162/neco.1996.8.4.773</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Neural+Computation&amp;rft.atitle=Semilinear+predictability+minimzation+produces+well-known+feature+detectors&amp;rft.volume=8&amp;rft.issue=4&amp;rft.pages=773-786&amp;rft.date=1996&amp;rft_id=info%3Adoi%2F10.1162%2Fneco.1996.8.4.773&amp;rft.aulast=Schmidhuber&amp;rft.aufirst=J%C3%BCrgen&amp;rft.au=Eldracher%2C+Martin&amp;rft.au=Foltin%2C+Bernhard&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-sch1990curiosity-44\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-sch1990curiosity_44-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation conference\">Schmidhuber, Jürgen (1990). <a class=\"external text\" href=\"http://people.idsia.ch/~juergen/FKI-126-90_(revised)bw_ocr.pdf\" rel=\"nofollow\">\"Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments\"</a> <span class=\"cs1-format\">(PDF)</span>. <i>TR FKI-126-90</i>. Tech. Univ. Munich.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=conference&amp;rft.atitle=Making+the+world+differentiable%3A+On+using+fully+recurrent+self-supervised+neural+networks+for+dynamic+reinforcement+learning+and+planning+in+non-stationary+environments&amp;rft.btitle=TR+FKI-126-90&amp;rft.pub=Tech.+Univ.+Munich&amp;rft.date=1990&amp;rft.aulast=Schmidhuber&amp;rft.aufirst=J%C3%BCrgen&amp;rft_id=http%3A%2F%2Fpeople.idsia.ch%2F~juergen%2FFKI-126-90_%28revised%29bw_ocr.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-sch1991curiosity-45\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-sch1991curiosity_45-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation conference\">Schmidhuber, Jürgen (1991). \"A possibility for implementing curiosity and boredom in model-building neural controllers\". <i>Proc. SAB'1991</i>. MIT Press/Bradford Books. pp. 222–227.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=conference&amp;rft.atitle=A+possibility+for+implementing+curiosity+and+boredom+in+model-building+neural+controllers&amp;rft.btitle=Proc.+SAB%271991&amp;rft.pages=222-227&amp;rft.pub=MIT+Press%2FBradford+Books&amp;rft.date=1991&amp;rft.aulast=Schmidhuber&amp;rft.aufirst=J%C3%BCrgen&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-bloomberg2018-46\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-bloomberg2018_46-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Vance, Ashlee (May 15, 2018). <a class=\"external text\" href=\"https://www.bloomberg.com/news/features/2018-05-15/google-amazon-and-facebook-owe-j-rgen-schmidhuber-a-fortune\" rel=\"nofollow\">\"Quote: It was another way of saying, Hey, kid, you didn't invent this\"</a>. <i>Bloomberg Business Week</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-01-16</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Bloomberg+Business+Week&amp;rft.atitle=Quote%3A+It+was+another+way+of+saying%2C+Hey%2C+kid%2C+you+didn%27t+invent+this.&amp;rft.date=2018-05-15&amp;rft.aulast=Vance&amp;rft.aufirst=Ashlee&amp;rft_id=https%3A%2F%2Fwww.bloomberg.com%2Fnews%2Ffeatures%2F2018-05-15%2Fgoogle-amazon-and-facebook-owe-j-rgen-schmidhuber-a-fortune&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-47\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-47\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Goodfellow, Ian. <a class=\"external text\" href=\"http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf\" rel=\"nofollow\">\"Generative Adversarial Networks\"</a> <span class=\"cs1-format\">(PDF)</span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=Generative+Adversarial+Networks&amp;rft.aulast=Goodfellow&amp;rft.aufirst=Ian&amp;rft_id=http%3A%2F%2Fwww.iangoodfellow.com%2Fslides%2F2016-12-04-NIPS.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-48\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-48\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Gutmann, Michael; Hyvärinen, Aapo. <a class=\"external text\" href=\"http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf\" rel=\"nofollow\">\"Noise-Contrastive Estimation\"</a> <span class=\"cs1-format\">(PDF)</span>. <i>International Conference on AI and Statistics</i>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=International+Conference+on+AI+and+Statistics&amp;rft.atitle=Noise-Contrastive+Estimation&amp;rft.aulast=Gutmann&amp;rft.aufirst=Michael&amp;rft.au=Hyv%C3%A4rinen%2C+Aapo&amp;rft_id=http%3A%2F%2Fproceedings.mlr.press%2Fv9%2Fgutmann10a%2Fgutmann10a.pdf&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-olli2010-49\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-olli2010_49-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Niemitalo, Olli (February 24, 2010). <a class=\"external text\" href=\"https://web.archive.org/web/20120312111546/http://yehar.com:80/blog/?p=167\" rel=\"nofollow\">\"A method for training artificial neural networks to generate missing data within a variable context\"</a>. <i>Internet Archive (Wayback Machine)</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">February 22,</span> 2019</span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Internet+Archive+%28Wayback+Machine%29&amp;rft.atitle=A+method+for+training+artificial+neural+networks+to+generate+missing+data+within+a+variable+context&amp;rft.date=2010-02-24&amp;rft.aulast=Niemitalo&amp;rft.aufirst=Olli&amp;rft_id=https%3A%2F%2Fweb.archive.org%2Fweb%2F20120312111546%2Fhttp%3A%2F%2Fyehar.com%3A80%2Fblog%2F%3Fp%3D167&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-Li-etal-GECCO2013-50\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-Li-etal-GECCO2013_50-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation conference\">Li, Wei; Gauci, Melvin; Gross, Roderich (July 6, 2013). \"A Coevolutionary Approach to Learn Animal Behavior Through Controlled Interaction\". <i>Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013)</i>. Amsterdam, The Netherlands: ACM. pp. 223–230. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.1145%2F2463372.2465801\" rel=\"nofollow\">10.1145/2463372.2465801</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=conference&amp;rft.atitle=A+Coevolutionary+Approach+to+Learn+Animal+Behavior+Through+Controlled+Interaction&amp;rft.btitle=Proceedings+of+the+15th+Annual+Conference+on+Genetic+and+Evolutionary+Computation+%28GECCO+2013%29&amp;rft.place=Amsterdam%2C+The+Netherlands&amp;rft.pages=223-230&amp;rft.pub=ACM&amp;rft.date=2013-07-06&amp;rft_id=info%3Adoi%2F10.1145%2F2463372.2465801&amp;rft.aulast=Li&amp;rft.aufirst=Wei&amp;rft.au=Gauci%2C+Melvin&amp;rft.au=Gross%2C+Roderich&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-51\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-51\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation arxiv\">Sajjadi, Mehdi S. M.; Schölkopf, Bernhard; Hirsch, Michael (2016-12-23). \"EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1612.07919\" rel=\"nofollow\">1612.07919</a></span> [<a class=\"external text\" href=\"//arxiv.org/archive/cs.CV\" rel=\"nofollow\">cs.CV</a>].</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=preprint&amp;rft.jtitle=arXiv&amp;rft.atitle=EnhanceNet%3A+Single+Image+Super-Resolution+Through+Automated+Texture+Synthesis&amp;rft.date=2016-12-23&amp;rft_id=info%3Aarxiv%2F1612.07919&amp;rft.aulast=Sajjadi&amp;rft.aufirst=Mehdi+S.+M.&amp;rft.au=Sch%C3%B6lkopf%2C+Bernhard&amp;rft.au=Hirsch%2C+Michael&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-52\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-52\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://medium.com/@alagraphy/this-person-does-not-exist-neither-will-anything-if-artificial-intelligence-keeps-learning-1a9fcba728f\" rel=\"nofollow\">\"This Person Does Not Exist: Neither Will Anything Eventually with AI\"</a>. March 20, 2019.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=This+Person+Does+Not+Exist%3A+Neither+Will+Anything+Eventually+with+AI&amp;rft.date=2019-03-20&amp;rft_id=https%3A%2F%2Fmedium.com%2F%40alagraphy%2Fthis-person-does-not-exist-neither-will-anything-if-artificial-intelligence-keeps-learning-1a9fcba728f&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-53\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-53\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://www.issuewire.com/artificial-intelligence-enters-the-history-of-art-1620667772563815\" rel=\"nofollow\">\"ARTificial Intelligence enters the History of Art\"</a>. December 28, 2018.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=ARTificial+Intelligence+enters+the+History+of+Art&amp;rft.date=2018-12-28&amp;rft_id=https%3A%2F%2Fwww.issuewire.com%2Fartificial-intelligence-enters-the-history-of-art-1620667772563815&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-54\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-54\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Tom Février (2019-02-17). <a class=\"external text\" href=\"https://link.medium.com/MYqBrGHIKV\" rel=\"nofollow\">\"Le scandale de l'intelligence ARTificielle\"</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=Le+scandale+de+l%27intelligence+ARTificielle&amp;rft.date=2019-02-17&amp;rft.au=Tom+F%C3%A9vrier&amp;rft_id=https%3A%2F%2Flink.medium.com%2FMYqBrGHIKV&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-55\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-55\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\"><a class=\"external text\" href=\"https://github.com/NVlabs/stylegan\" rel=\"nofollow\">\"StyleGAN: Official TensorFlow Implementation\"</a>. March 2, 2019  – via GitHub.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=StyleGAN%3A+Official+TensorFlow+Implementation&amp;rft.date=2019-03-02&amp;rft_id=https%3A%2F%2Fgithub.com%2FNVlabs%2Fstylegan&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-TPDNEitboowo2019-56\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-TPDNEitboowo2019_56-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Paez, Danny (2019-02-13). <a class=\"external text\" href=\"https://www.inverse.com/article/53280-this-person-does-not-exist-gans-website=website=Inverse\" rel=\"nofollow\">\"This Person Does Not Exist Is the Best One-Off Website of 2019\"</a><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-02-16</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&amp;rft.genre=unknown&amp;rft.btitle=This+Person+Does+Not+Exist+Is+the+Best+One-Off+Website+of+2019&amp;rft.date=2019-02-13&amp;rft.aulast=Paez&amp;rft.aufirst=Danny&amp;rft_id=https%3A%2F%2Fwww.inverse.com%2Farticle%2F53280-this-person-does-not-exist-gans-website%3Dwebsite%3DInverse&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-TPDNE-57\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-TPDNE_57-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">BESCHIZZA, ROB (2019-02-15). <a class=\"external text\" href=\"https://boingboing.net/2019/02/15/this-person-does-not-exist.html\" rel=\"nofollow\">\"This Person Does Not Exist\"</a>. <i>Boing-Boing</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-02-16</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Boing-Boing&amp;rft.atitle=This+Person+Does+Not+Exist&amp;rft.date=2019-02-15&amp;rft.aulast=BESCHIZZA&amp;rft.aufirst=ROB&amp;rft_id=https%3A%2F%2Fboingboing.net%2F2019%2F02%2F15%2Fthis-person-does-not-exist.html&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-Style-based_GANs_–_Generating_and_Tuning_Realistic_Artificial_Faces-58\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-Style-based_GANs_–_Generating_and_Tuning_Realistic_Artificial_Faces_58-0\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation web\">Horev, Rani (2018-12-26). <a class=\"external text\" href=\"https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/\" rel=\"nofollow\">\"Style-based GANs – Generating and Tuning Realistic Artificial Faces\"</a>. <i>Lyrn.AI</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2019-02-16</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=unknown&amp;rft.jtitle=Lyrn.AI&amp;rft.atitle=Style-based+GANs+%E2%80%93+Generating+and+Tuning+Realistic+Artificial+Faces&amp;rft.date=2018-12-26&amp;rft.aulast=Horev&amp;rft.aufirst=Rani&amp;rft_id=https%3A%2F%2Fwww.lyrn.ai%2F2018%2F12%2F26%2Fa-style-based-generator-architecture-for-generative-adversarial-networks%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-59\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-59\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation arxiv\">Elgammal, Ahmed; Liu, Bingchen; Elhoseiny, Mohamed; Mazzone, Marian (2017). \"CAN: Creative Adversarial Networks, Generating \"Art\" by Learning About Styles and Deviating from Style Norms\". <a href=\"/wiki/ArXiv\" title=\"ArXiv\">arXiv</a>:<span class=\"cs1-lock-free\" title=\"Freely accessible\"><a class=\"external text\" href=\"//arxiv.org/abs/1706.07068\" rel=\"nofollow\">1706.07068</a></span> [<a class=\"external text\" href=\"//arxiv.org/archive/cs.AI\" rel=\"nofollow\">cs.AI</a>].</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=preprint&amp;rft.jtitle=arXiv&amp;rft.atitle=CAN%3A+Creative+Adversarial+Networks%2C+Generating+%22Art%22+by+Learning+About+Styles+and+Deviating+from+Style+Norms&amp;rft.date=2017&amp;rft_id=info%3Aarxiv%2F1706.07068&amp;rft.aulast=Elgammal&amp;rft.aufirst=Ahmed&amp;rft.au=Liu%2C+Bingchen&amp;rft.au=Elhoseiny%2C+Mohamed&amp;rft.au=Mazzone%2C+Marian&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-60\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-60\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Cohn, Gabe (2018-10-25). <a class=\"external text\" href=\"https://www.nytimes.com/2018/10/25/arts/design/ai-art-sold-christies.html\" rel=\"nofollow\">\"AI Art at Christie's Sells for $432,500\"</a>. <i>The New York Times</i>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=The+New+York+Times&amp;rft.atitle=AI+Art+at+Christie%27s+Sells+for+%24432%2C500&amp;rft.date=2018-10-25&amp;rft.aulast=Cohn&amp;rft.aufirst=Gabe&amp;rft_id=https%3A%2F%2Fwww.nytimes.com%2F2018%2F10%2F25%2Farts%2Fdesign%2Fai-art-sold-christies.html&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-61\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-61\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation journal\">Mazzone, Marian and Ahmed Elgammal (21 February 2019). \"Art, Creativity, and the Potential of Artificial Intelligence\". <i>Arts</i>. <b>8</b>: 26. <a href=\"/wiki/Digital_object_identifier\" title=\"Digital object identifier\">doi</a>:<a class=\"external text\" href=\"//doi.org/10.3390%2Farts8010026\" rel=\"nofollow\">10.3390/arts8010026</a>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=Arts&amp;rft.atitle=Art%2C+Creativity%2C+and+the+Potential+of+Artificial+Intelligence&amp;rft.volume=8&amp;rft.pages=26&amp;rft.date=2019-02-21&amp;rft_id=info%3Adoi%2F10.3390%2Farts8010026&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><span class=\"cs1-maint citation-comment\">CS1 maint: Uses authors parameter (<a href=\"/wiki/Category:CS1_maint:_Uses_authors_parameter\" title=\"Category:CS1 maint: Uses authors parameter\">link</a>)</span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "<li id=\"cite_note-62\"><span class=\"mw-cite-backlink\"><b><a href=\"#cite_ref-62\">^</a></b></span> <span class=\"reference-text\"><cite class=\"citation news\">Kulp, Patrick (May 23, 2019). <a class=\"external text\" href=\"https://www.adweek.com/digital/samsungs-ai-lab-can-create-fake-video-footage-from-a-single-headshot/\" rel=\"nofollow\">\"Samsung's AI Lab Can Create Fake Video Footage From a Single Headshot\"</a>. AdWeek.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.atitle=Samsung%27s+AI+Lab+Can+Create+Fake+Video+Footage+From+a+Single+Headshot&amp;rft.date=2019-05-23&amp;rft.aulast=Kulp&amp;rft.aufirst=Patrick&amp;rft_id=https%3A%2F%2Fwww.adweek.com%2Fdigital%2Fsamsungs-ai-lab-can-create-fake-video-footage-from-a-single-headshot%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></span>\n",
       "</li>\n",
       "</ol></div>\n",
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       "<ul><li><cite class=\"citation news\">Knight, Will. <a class=\"external text\" href=\"https://www.technologyreview.com/s/603216/5-big-predictions-for-artificial-intelligence-in-2017/\" rel=\"nofollow\">\"5 Big Predictions for Artificial Intelligence in 2017\"</a>. <i>MIT Technology Review</i><span class=\"reference-accessdate\">. Retrieved <span class=\"nowrap\">2017-01-05</span></span>.</cite><span class=\"Z3988\" title=\"ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft.jtitle=MIT+Technology+Review&amp;rft.atitle=5+Big+Predictions+for+Artificial+Intelligence+in+2017&amp;rft.aulast=Knight&amp;rft.aufirst=Will&amp;rft_id=https%3A%2F%2Fwww.technologyreview.com%2Fs%2F603216%2F5-big-predictions-for-artificial-intelligence-in-2017%2F&amp;rfr_id=info%3Asid%2Fen.wikipedia.org%3AGenerative+adversarial+network\"></span><link href=\"mw-data:TemplateStyles:r886058088\" rel=\"mw-deduplicated-inline-style\"/></li>\n",
       "<li><a class=\"external text\" href=\"https://arxiv.org/abs/1812.04948\" rel=\"nofollow\">A Style-Based Generator Architecture for Generative Adversarial Networks</a></li>\n",
       "<li><a class=\"external text\" href=\"https://www.thispersondoesnotexist.com/\" rel=\"nofollow\">This Person Does Not Exist</a> –  photorealistic images of people who do not exist, generated by <a href=\"/wiki/StyleGAN\" title=\"StyleGAN\">StyleGAN</a></li>\n",
       "<li><a class=\"external text\" href=\"https://arxiv.org/abs/1906.01529\" rel=\"nofollow\">Generative Adversarial Networks: A Survey and Taxonomy</a></li></ul>\n",
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