https://github.com/andersy005/pytorch-gan

Pytorch implementations of Generative Adversarial Networks.
https://github.com/andersy005/pytorch-gan

Last synced: about 1 year ago
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Pytorch implementations of Generative Adversarial Networks.

• Host: GitHub
• URL: https://github.com/andersy005/pytorch-gan
• Owner: andersy005
• License: mit
• Created: 2018-02-28T16:36:15.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-03-05T06:20:08.000Z (about 8 years ago)
• Last Synced: 2025-03-28T23:37:56.009Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 395 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# Pytorch-GAN

Pytorch implementations of Generative Adversarial Networks.

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/andersy005/Pytorch-GAN/master)

## About

This repo contains Pytorch implementations of Generative Adversarial Networks (GANs) suggested in research papers. The implementations are adapted from [Erik Linder-Norén's Keras-GAN repo](https://github.com/eriklindernoren/Keras-GAN).

Throughout the implementations, I will try to follow Erik's philosophy:

> If dense layers produce reasonable results for a given model I will often prefer them over convolutional layers. The reason is that I would like to enable people without GPUs to test these implementations out. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. However, because of this the results will not always be as nice as in the papers.

## Table of Contents

- [Pytorch-GAN](#pytorch-gan)

  * [About](#about)

  * [Table of Contents](#table-of-contents)

  * [Installation](#installation)

  * [Implementations](#implementations)

    + [ ] [Auxiliary Classifier GAN](#ac-gan)

    + [ ] [Adversarial Autoencoder](#adversarial-autoencoder)

    + [ ] [Bidirectional GAN](#bigan)

    + [ ] [Boundary-Seeking GAN](#bgan)

    + [ ] [Conditional GAN](#cgan)

    + [ ] [Context-Conditional GAN](#cc-gan)

    + [ ] [Context Encoder](#context-encoder)

    + [ ] [Coupled GANs](#cogan)

    + [ ] [CycleGAN](#cyclegan)

    + [ ] [Deep Convolutional GAN](#dcgan)

    + [ ] [DualGAN](#dualgan)

    + [ ] [Generative Adversarial Network](#gan)

    + [ ] [InfoGAN](#infogan)

    + [ ] [LSGAN](#lsgan)

    + [ ] [Pix2Pix](#pix2pix)

    + [ ] [Semi-Supervised GAN](#sgan)

    + [ ] [Wasserstein GAN](#wgan)