This repo is a collection of the implementations of many GANs. In order to make the codes easy to read and follow, I minimize the code and run on the same MNIST dataset.
What does the MNIST data look like?
Toy implementations are organized as following:
1. Base Method
2. Loss or Structure Modifications
- Least Squares GAN (LSGAN)
- Wasserstein GAN (WGAN)
- Self-Attention GAN (SAGAN)
- Progressive-Growing GAN (PGGAN)
3. Can be Conditional
4. Image to Image Transformation
$ git clone https://github.com/MorvanZhou/mnistGANs
$ cd mnistGANs/
$ pip3 install -r requirements.txt
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Least Squares Generative Adversarial Networks
Improved Training of Wasserstein GANs
Wasserstein Divergence for GANs
Self-Attention Generative Adversarial Networks
PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
Conditional Generative Adversarial Nets
Conditional Image Synthesis with Auxiliary Classifier GANs
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
A Style-Based Generator Architecture for Generative Adversarial Networks
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Image-to-Image Translation with Conditional Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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