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README
MIT

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

MIT License Copyright (c) 2020 Morvan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Python
MIT
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