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Implementation of Conditional Generative Adversarial Networks in PyTorch

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Conditional Deep Convolutional Generative Adversarial Network

Conditional Generation of MNIST images using conditional DC-GAN in PyTorch.

Based on the following papers:

Implementation inspired by the PyTorch examples implementation of DCGAN.

Sample Results

Example of sampling results shown below. Each row is conditioned on a different digit label: Example of sampling results

Usage

python conditional_dcgan.py --cuda --save_dir=models --samples_dir=samples --epochs=25

Questions and comments:

Feel free to reach to me at malzantot [at] ucla [dot] edu for any questions or comments.

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