A curated list of state-of-the-art publications and resources about
Generative Adversarial Networks (GANs) and their applications.
Overview
Generative models are models that can learn to create data that is
similar to data that we give them. One of the most promising approaches
of those models are Generative Adversarial Networks (GANs), a branch of
unsupervised machine learning implemented by a system of two neural
networks competing against each other in a zero-sum game framework. They
were first introduced by Ian Goodfellow et al. in 2014. This repository
aims at presenting an elaborate list of the state-of-the-art works on
the field of Generative Adversarial Networks since their introduction in
2014.
Image taken from http://multithreaded.stitchfix.com/blog/2016/02/02/a-fontastic-voyage/
This is going to be an evolving post and I will keep
updating it (at least twice monthly) so make sure you have starred and
forked this repository on GitHub before moving on !
Contributing
Contributions are welcome !! If you have any suggestions (missing or
new papers, missing repos or typos) you can pull a request or start a
discussion.
Opening Publication
Generative Adversarial Nets (GANs) (2014) [pdf] [presentation] [code] [video]
State-of-the-art papers (Descending order based on Google Scholar Citations)
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGANs) (2015) [pdf]
Explaining and Harnessing Adversarial Examples(2014) [pdf]
Semi-Supervised Learning with Deep Generative Models*1 (2014) [pdf]
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (LAPGAN) (2015) [pdf]
Improved Techniques for Training GANs (2016) [pdf]
Conditional Generative Adversarial Nets (CGAN) (2014) [pdf]
Generative Moment Matching Networks (2015) [pdf]
Deep multi-scale video prediction beyond mean square error (2015) [pdf]
Autoencoding beyond pixels using a learned similarity metric (VAE-GAN) (2015) [pdf]
Adversarial Autoencoders (2015) [pdf]
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016) [pdf]
Context Encoders: Feature Learning by Inpainting (2016) [pdf]
Generating Images with Perceptual Similarity Metrics based on Deep Networks (2016) [pdf]
Energy-based Generative Adversarial Network (EBGAN) (2016) [pdf]
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN) (2016) [pdf]
Generative Adversarial Text to Image Synthesis (2016) [pdf]
Conditional Image Generation with PixelCNN Decoders (2015) [pdf]
Generative Image Modeling using Style and Structure Adversarial Networks (S^2GAN) (2016) [pdf]
Adversarial Feature Learning (BiGAN) (2016) [pdf]
Improving Variational Inference with Inverse Autoregressive Flow (2016) [pdf]
Theory
Improved Techniques for Training GANs [pdf]
Energy-Based GANs & other Adversarial things by Yann Le Cun [pdf]
Mode RegularizedGenerative Adversarial Networks [pdf]
Presentations
Courses / Tutorials / Blogs (Webpages unless other is stated)
Resources / Models (GitHub repositories unless other is stated)
Frameworks & Libraries (Descending order based on GitHub stars)
链接：
http://gkalliatakis.com/blog/delving-deep-into-gans
github链接：
https://github.com/GKalliatakis/Delving-deep-into-GANs
原文链接：
http://weibo.com/1402400261/F183KCisr?from=page_1005051402400261_profile&wvr=6&mod=weibotime&type=comment#_rnd1493720823900
