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  • Rank 185,410 (Top 4 %)
  • Language
    Python
  • License
    MIT License
  • Created about 4 years ago
  • Updated about 1 year ago

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Repository Details

Some implementations of GAN.

GANs implementation using MNIST data

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

3. Can be Conditional

4. Image to Image Transformation

Installation

$ git clone https://github.com/MorvanZhou/mnistGANs
$ cd mnistGANs/
$ pip3 install -r requirements.txt

GAN

Generative Adversarial Nets

code - gif result

DCGAN

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

code - gif result

LSGAN

Least Squares Generative Adversarial Networks

code - gif result

WGAN

Wasserstein GAN

code - gif result

WGANpg

Improved Training of Wasserstein GANs

code - gif result

WGANdiv

Wasserstein Divergence for GANs

code - gif result

SAGAN

Self-Attention Generative Adversarial Networks

code - gif result

PGGAN

PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION

code - gif result

CGAN

Conditional Generative Adversarial Nets

code - gif result

ACGAN

Conditional Image Synthesis with Auxiliary Classifier GANs

code - gif result

InfoGAN

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

code - gif result

StyleGAN

A Style-Based Generator Architecture for Generative Adversarial Networks

code - gif result

CCGAN

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

code - gif result

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

code - gif result

CycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

code - gif result

SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

code - gif result

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