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
  • Overview
  • GAN Theory
  • Training GANs
  • GAN Architectures
  • Conditional GANs
  • Latent Space
  • Evaluation
  • Others
  • Applications Using GANs
    • Image Synthesis
    • Image translation
    • Domain Adaptation
    • Super Resolution
    • Semi-Supervised Learning
    • Low Level Image Process
    • Text to Image
    • Object Detection
    • Semantic Segmentation
    • Medical Images
    • Other Applications
• Other Resources
• Selected

Papers

Overview

• How Generative Adversarial Networks and Their Variants Work: An Overview [arXiv 13 Nov 2018]

GAN Theory

Training GANs

• Improved Techniques for Training GANs [NIPS2016] [Pytorch]

GAN Architectures

• Domain Partitioning Network [arXiv]
• A Style-Based Generator Architecture for Generative Adversarial Networks [arXiv 12 Dec 2018]
• Self-Attention Generative Adversarial Networks [arXiv 21 May 2018]

Conditional GANs

• Diversity-Sensitive Conditional Generative Adversarial Networks [ICLR2019]
• Robust Conditional Generative Adversarial Networks [ICLR2019]
• Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation [ICLR2019]
• cGANs with Projection Discriminator [ICLR2018]

Latent Space

Evaluation

Others

• Self-Supervised Generative Adversarial Networks [CVPR2019]
• Label-Removed Generative Adversarial Networks Incorporating with K-Means [arXiv 19 Feb 2019]

Applications Using GANs

Image Synthesis

• Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis [CVPR2019]

Image translation

• CollaGAN : Collaborative GAN for Missing Image Data Imputation [CVPR2019]

Domain Adaptation

Super-resolution

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [CVPR2017] [Pytorch] [Pytorch]
• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks [ECCV2018 workshop] [Pytorch]

Semi-supervised Learning

• Good Semi-supervised Learning That Requires a Bad GAN [NIPS2017]
• Triple Generative Adversarial Nets [NIPS2017]
• Improved Techniques for Training GANs [NIPS2016]
• Semi-Supervised Learning with Generative Adversarial Networks [arXiv 2016]
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks [arXiv 2015]

Low Level Image Process

Text to Image

• Generative Adversarial Text to Image Synthesis [ICML2016] [Pytorch]

Object Detection

Semantic Segmentation

Data Augmentation

• Learning to Compose Domain-Specific Transformations for Data Augmentation[NIPS2017]
• BAGAN: Data Augmentation with Balancing GAN [arXiv 5 Jun 2018]

Medical Images

Survey

• Generative Adversarial Network in Medical Imaging: A Review [arXiv 19 Sep 2018]

Image Classification

• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification [arXiv 3 Mar 2018]
• Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification [ISBI 2018]

Others

• General-to-Detailed GAN for Infrequent Class Medical Images [arXiv 28 Nov 2018]

Other Applications

• MEAL: Multi-Model Ensemble via Adversarial Learning [AAAI2019] [Pytorch(official)]

Other Resources

the-gan-zoo

GAN-Timeline

really-awesome-gan

AdversarialNetsPapers

Awesome GAN for Medical Imaging

Selected

• A Style-Based Generator Architecture for Generative Adversarial Networks [arXiv 12 Dec 2018]
• Supervised Generative Adversarial Networks [CVPR2019]
• Self-Attention Generative Adversarial Networks [arXiv 21 May 2018]