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https://github.com/zhaoxin94/awesome-gan

A collection of AWESOME things about GAN
https://github.com/zhaoxin94/awesome-gan

List: awesome-gan

deep-learning gan generative-adversarial-network image-translation

Last synced: about 1 month ago
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A collection of AWESOME things about GAN

Host: GitHub
URL: https://github.com/zhaoxin94/awesome-gan
Owner: zhaoxin94
Created: 2018-07-16T02:52:09.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2019-04-27T12:13:17.000Z (over 5 years ago)
Last Synced: 2024-05-21T08:33:50.708Z (7 months ago)
Topics: deep-learning, gan, generative-adversarial-network, image-translation
Homepage:
Size: 31.3 KB
Stars: 47
Watchers: 6
Forks: 15
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-of-awesome-ml - awesome-gan (by zhaoxin94)
ultimate-awesome - awesome-gan - A collection of AWESOME things about GAN. (Other Lists / PowerShell Lists)

README

        # awesome-gan

This repo is a collection of AWESOME things about Generative Adversarial Networks,including papers,code etc.Feel free to star and fork.

# Contents

- [Papers](#papers)

  - [Overview](#overview)

  - [GAN Theory](#gan-theory)

  - [Training GANs](#training-gans)

  - [GAN Architectures](#gan-architectures)

  - [Conditional GANs](#conditional-gans)

  - [Latent Space](#latent-space)

  - [Evaluation](#evaluation)

  - [Others](#others)

  - [Applications Using GANs](#applications-using-gans)

    - [Image Synthesis](#image-synthesis)

    - [Image translation](#image-translation)

    - [Domain Adaptation](#domain-adaptation)

    - [Super Resolution](#super-resolution)

    - [Semi-Supervised Learning](#semi-supervised-learning)

    - [Low Level Image Process](#low-level-image-process)

    - [Text to Image](#text-to-image)

    - [Object Detection](#object-detection)

    - [Semantic Segmentation](#semantic-segmentation)

    - [Medical Images](#medical-images)

    - [Other Applications](#other-applications)

- [Other Resources](#other-resources)

- [Selected](#selected)

# Papers

## Overview

- How Generative Adversarial Networks and Their Variants Work: An Overview [[arXiv 13 Nov 2018]](https://arxiv.org/abs/1711.05914v9)

## GAN Theory

## Training GANs

- Improved Techniques for Training GANs [[NIPS2016]](https://papers.nips.cc/paper/6125-improved-techniques-for-training-gans) [[Pytorch]](https://github.com/Sleepychord/ImprovedGAN-pytorch)

## GAN Architectures

- Domain Partitioning Network [[arXiv]](https://arxiv.org/abs/1902.08134v1)

- A Style-Based Generator Architecture for Generative Adversarial Networks [[arXiv 12 Dec 2018]](https://arxiv.org/abs/1812.04948v1)

- Self-Attention Generative Adversarial Networks [[arXiv 21 May 2018]](https://arxiv.org/abs/1805.08318)

## Conditional GANs

- Diversity-Sensitive Conditional Generative Adversarial Networks  [[ICLR2019]](https://openreview.net/forum?id=rJliMh09F7)

- Robust Conditional Generative Adversarial Networks [[ICLR2019]](https://openreview.net/forum?id=Byg0DsCqYQ)

- Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation [[ICLR2019]](https://openreview.net/forum?id=HJxyAjRcFX)

- cGANs with Projection Discriminator [[ICLR2018]](https://openreview.net/forum?id=ByS1VpgRZ)

## Latent Space

## Evaluation

## Others

- Self-Supervised Generative Adversarial Networks [[CVPR2019]](https://arxiv.org/pdf/1811.11212.pdf)

- Label-Removed Generative Adversarial Networks Incorporating with K-Means [[arXiv 19 Feb 2019]](https://arxiv.org/abs/1902.06938v1)

## Applications Using GANs

### Image Synthesis

- Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis [[CVPR2019]](https://arxiv.org/abs/1903.05628v1)

### Image translation

- CollaGAN : Collaborative GAN for Missing Image Data Imputation [[CVPR2019]](https://arxiv.org/abs/1901.09764)

### Domain Adaptation

### Super-resolution

- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [[CVPR2017]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf) [[Pytorch]](https://github.com/leftthomas/SRGAN) [[Pytorch]](https://github.com/aitorzip/PyTorch-SRGAN)

- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [[ECCV2018 workshop]](https://arxiv.org/abs/1809.00219) [[Pytorch]](https://github.com/xinntao/ESRGAN)

### Semi-supervised Learning

- Good Semi-supervised Learning That Requires a Bad GAN [[NIPS2017]](http://papers.nips.cc/paper/7229-good-semi-supervised-learning-that-requires-a-bad-gan)

- Triple Generative Adversarial Nets [[NIPS2017]](https://papers.nips.cc/paper/6997-triple-generative-adversarial-nets)

- Improved Techniques for Training GANs [[NIPS2016]](https://papers.nips.cc/paper/6125-improved-techniques-for-training-gans)

- Semi-Supervised Learning with Generative Adversarial Networks [[arXiv 2016]](https://arxiv.org/abs/1606.01583)

- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks [[arXiv 2015]](https://arxiv.org/abs/1511.06390) 

### Low Level Image Process

### Text to Image

- Generative Adversarial Text to Image Synthesis [[ICML2016]](http://proceedings.mlr.press/v48/reed16.pdf) [[Pytorch]](https://github.com/aelnouby/Text-to-Image-Synthesis)

### Object Detection

### Semantic Segmentation

### Data Augmentation

- Learning to Compose Domain-Specific Transformations for Data Augmentation[[NIPS2017]](https://papers.nips.cc/paper/6916-learning-to-compose-domain-specific-transformations-for-data-augmentation.pdf)

- BAGAN: Data Augmentation with Balancing GAN [[arXiv 5 Jun 2018]](https://arxiv.org/abs/1803.09655v2)

### Medical Images

#### Survey

- Generative Adversarial Network in Medical Imaging: A Review [[arXiv 19 Sep 2018]](https://arxiv.org/abs/1809.07294v1)

#### Image Classification

- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification [[arXiv 3 Mar 2018]](https://arxiv.org/abs/1803.01229)

- Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification [[ISBI 2018]](https://arxiv.org/abs/1801.02385v1)

#### Others

- General-to-Detailed GAN for Infrequent Class Medical Images [[arXiv 28 Nov 2018]](https://arxiv.org/abs/1812.01690)

### Other Applications

- MEAL: Multi-Model Ensemble via Adversarial Learning [[AAAI2019]](https://arxiv.org/abs/1812.02425) [[Pytorch(official)]](https://github.com/AaronHeee/MEAL)

# Other Resources

[the-gan-zoo](https://github.com/hindupuravinash/the-gan-zoo)

[GAN-Timeline](https://github.com/dongb5/GAN-Timeline)

[really-awesome-gan](https://github.com/nightrome/really-awesome-gan)

[AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers)

[Awesome GAN for Medical Imaging](https://github.com/xinario/awesome-gan-for-medical-imaging)

# Selected

- A Style-Based Generator Architecture for Generative Adversarial Networks [[arXiv 12 Dec 2018]](https://arxiv.org/abs/1812.04948v1)

- Supervised Generative Adversarial Networks [[CVPR2019]](https://arxiv.org/pdf/1811.11212.pdf)

- Self-Attention Generative Adversarial Networks [[arXiv 21 May 2018]](https://arxiv.org/abs/1805.08318)