GANs Tutorial

Very simple implementation of GANs, DCGANs, CGANs, WGANs, and etc. with PyTorch for only MNIST.

You can run it immediately at Jupyter Notebook. And actually you can also run these codes by using Google Colab immediately!

Requirements

• python 3.6 (Anaconda)
• pytorch 1.0.0 (updated from 0.4.0. If you want to use the previous version, then find previous commit.)

Implementation List

GANs

• Vanilla GAN
• Conditional GANs
• DCGAN
• Wasserstain GAN
• WGAN-gp
• infoGAN

Experimental Samples

• You can see the samples at ipynbs.
• After DCGAN, DCGAN with condition is a base model.
• Trained 30 epochs respectively.

Vanilla GAN

Conditional GAN

DC GAN

WGAN-gp

infoGAN w/ walking code 1

infoGAN w/ walking code 2

Colab