Those code implement multiple gan including vanilla_gan, dcgan, cgan, infogan and wgan. I only use mnist dataset and if I have enough time later, I will update other dataset.
Paper: Generative Adversarial Nets
python3 train.py vanilla_gan mnist
Paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
python3 train.py dcgan mnist
Paper: Conditional Generative Adversarial Nets
python3 train.py cgan mnist
Paper: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
python3 train.py infogan mnist
Paper: Wasserstein GAN
python3 train.py wgan mnist
