Note: go to wiki for loss curve and sample result
conda env create -f environment.yml
GAN model according to Generative Adversarial Nets
classification model according the example of pytorch
VAE model according to Auto-encoding variational bayes
DCGAN model according to unsupervised representation learning with deep convolutional generative adversarial
improved DCGAN model accdording to improved Techniques for Training GANs
WGAN model according Wasserstein GAN
WGAN-GP model according to Improved Training of Wasserstein GAN
LSGAN model according to least square generative adversarial net
BEGAN model according to BEGAN:Boundary Equilibrium Generative Adversarial Networks
InfoGAN model according to InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets