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A collection of AWESOME things about GAN

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# 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)

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