https://github.com/Faldict/awesome-GAN

A curated list of awesome Generative Adversarial Nets resources.
https://github.com/Faldict/awesome-GAN

List: awesome-GAN

awesome

Last synced: 3 days ago
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A curated list of awesome Generative Adversarial Nets resources.

• Host: GitHub
• URL: https://github.com/Faldict/awesome-GAN
• Owner: Faldict
• Created: 2017-03-16T02:33:00.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-09-25T03:47:43.000Z (about 6 years ago)
• Last Synced: 2024-05-21T08:33:51.414Z (6 months ago)
• Topics: awesome
• Size: 12.7 KB
• Stars: 60
• Watchers: 5
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-of-awesome-ml - awesome-GAN (by Faldict)
• ultimate-awesome - awesome-GAN - A curated list of awesome Generative Adversarial Nets resources. (Other Lists / PowerShell Lists)

README

        
# awesome-GAN    [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of awesome Generative Adversarial Nets resources.

## Contributing

Please feel free to send me [pull request](https://github.com/Faldict/awesome-GAN/pulls) to add links.

## Contents

- [Tutorials](#Tutorials)

- [Papers](#Papers)

- [Codes](#Codes)

## Tutorials

- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://arxiv.org/abs/1701.00160)

- [ganhacks](https://github.com/soumith/ganhacks) - How to Train a GAN? Tips and tricks to make GANs work

- [Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)](https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f)

- [GAN Zoo](https://deephunt.in/the-gan-zoo-79597dc8c347) - A list of all named GANs!

- [GANs in Action](https://www.manning.com/books/gans-in-action)-A book that teaches you how to build and train your own generative adversarial networks

## Papers

- [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) - GAN

- [Deep Generative Image Models using Lapalacian Pyramid of Adversarial Networks](https://arxiv.org/abs/1506.05751) - LAPGAN

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) - DCGAN

- [Generative Adversarial Text to Image Synthesis](https://arxiv.org/abs/1605.05396)

- [Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)

- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://arxiv.org/abs/1609.05473) - SeqGAN

- [Connecting Generative Adversarial Networks and Actor-Critic Methods](https://arxiv.org/abs/1610.01945v1)

- [A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models](https://arxiv.org/abs/1611.03852)

- [Wasserstein GAN](https://arxiv.org/abs/1701.07875) - A new algorithm named WGAN, an alternative to traditional GAN training.

- [Generative Adversarial Active Learning](https://arxiv.org/abs/1702.07956) - GAAL

- [Generalization and Equilibrium in Generative Adversarial Nets](https://arxiv.org/abs/1703.00573)

- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://junyanz.github.io/CycleGAN/)

- [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/abs/1703.10717) - BEGAN : State of the art generation of faces with Generative Adversarial Networks

- [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://arxiv.org/abs/1705.10513) - IRGAN : Best Paper in SIGIR

- [Adversarially Regularized Autoencoders for Generating Discrete Structures](https://arxiv.org/abs/1706.04223) - ARAE

- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://arxiv.org/abs/1706.07068) - CAN

- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://arxiv.org/abs/1711.08267) - GraphGAN

- [ComboGAN: Unrestrained Scalability for Image Domain Translation](https://arxiv.org/pdf/1712.06909.pdf) - ComboGAN

- [CapsuleGAN: Generative Adversarial Capsule Network](https://arxiv.org/pdf/1802.06167v3.pdf) - CapsuleGAN

## Codes

- [DCGAN - PyTorch](https://github.com/pytorch/examples/tree/master/dcgan) - An implement of DCGAN by PyTorch.

- [imitation](https://github.com/openai/imitation) - A implementation of the paper [Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476)

- [Wasserstein GAN](https://github.com/martinarjovsky/WassersteinGAN) - Code accompanying the paper ["Wasserstein GAN"](https://arxiv.org/abs/1701.07875)

- [CycleGAN](https://github.com/junyanz/CycleGAN) - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more (from UC Berkeley)

- [CycleGAN and pix2pix in PyTorch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is ongoing PyTorch implementation for both unpaired and paired image-to-image translation.

- [ComboGAN](https://github.com/AAnoosheh/ComboGAN) - ComboGAN

- [CapsuleGAN](https://github.com/Faldict/CapsuleGAN) - My own implementation on CapsuleGAN.