In this course I've learned all about GANs, how to build them from scratch and how to enhance enhance them.
* Generator, Discriminator tradeoff
* Generative loss, metrics
* Conditional Generation
* Feature spaces
* Data Augmentation effect
* Advanced use cases of GANs
* Vanilla GAN
* DCGAN
* Conditional GAN
* SN-GAN
* WGAN-GP
* FID Evaluation metric
* PPL Evaluation metric
* StyleGAN
* VAE
* Pix2Pix
* U-Nets with GANs
* CycleGAN
* Pix2PixHD
* GauGAN
* SRGAN