Papers

Max-margin Deep Generative Models

• arxiv: http://arxiv.org/abs/1504.06787
• github: https://github.com/zhenxuan00/mmdgm

Discriminative Regularization for Generative Models

• arxiv: http://arxiv.org/abs/1602.03220
• github: https://github.com/vdumoulin/discgen

Auxiliary Deep Generative Models

• arxiv: http://arxiv.org/abs/1602.05473
• github: https://github.com/larsmaaloee/auxiliary-deep-generative-models

Sampling Generative Networks: Notes on a Few Effective Techniques

• arxiv: http://arxiv.org/abs/1609.04468
• paper: https://github.com/dribnet/plat

Conditional Image Synthesis With Auxiliary Classifier GANs

• arxiv: https://arxiv.org/abs/1610.09585
• github: https://github.com/buriburisuri/ac-gan
• github(Keras): https://github.com/lukedeo/keras-acgan

On the Quantitative Analysis of Decoder-Based Generative Models

• intro: University of Toronto & OpenAI & CMU
• arxiv: https://arxiv.org/abs/1611.04273
• github: https://github.com/tonywu95/eval_gen

Boosted Generative Models

• paper: https://openreview.net/pdf?id=HyY4Owjll

An Architecture for Deep, Hierarchical Generative Models

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1612.04739
• github: https://github.com/Philip-Bachman/MatNets-NIPS

Deep Learning and Hierarchal Generative Models

• intro: MIT
• arxiv: https://arxiv.org/abs/1612.09057

Generative Adversarial Network

Generative Adversarial Networks

Generative Adversarial Nets

• arxiv: http://arxiv.org/abs/1406.2661
• paper: https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
• github: https://github.com/goodfeli/adversarial
• github: https://github.com/aleju/cat-generator

Adversarial Feature Learning

• intro: ICLR 2017
• arxiv: https://arxiv.org/abs/1605.09782
• github: https://github.com/jeffdonahue/bigan

Generative Adversarial Networks

• intro: by Ian Goodfellow, NIPS 2016 tutorial
• arxiv: https://arxiv.org/abs/1701.00160
• slides: http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf
• mirror: https://pan.baidu.com/s/1gfBNYW7

Adversarial Examples and Adversarial Training

• intro: NIPS 2016, Ian Goodfellow OpenAI
• slides: http://www.iangoodfellow.com/slides/2016-12-9-AT.pdf

How to Train a GAN? Tips and tricks to make GANs work

• github: https://github.com/soumith/ganhacks

CatGAN

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

• arxiv: http://arxiv.org/abs/1511.06390

DCGAN

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

• arxiv: http://arxiv.org/abs/1511.06434
• github: https://github.com/jazzsaxmafia/dcgan_tensorflow
• github: https://github.com/Newmu/dcgan_code
• github: https://github.com/mattya/chainer-DCGAN
• github: https://github.com/soumith/dcgan.torch
• github: https://github.com/carpedm20/DCGAN-tensorflow

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

• arxiv: https://arxiv.org/abs/1606.03657
• github: https://github.com/openai/InfoGAN
• github(Tensorflow): https://github.com/buriburisuri/supervised_infogan

Learning Interpretable Latent Representations with InfoGAN: A tutorial on implementing InfoGAN in Tensorflow

• blog: https://medium.com/@awjuliani/learning-interpretable-latent-representations-with-infogan-dd710852db46#.r0kur3aum
• github: https://gist.github.com/awjuliani/c9ecd8b37d33d6855cd4ed9aa16ce89f#file-infogan-tutorial-ipynb

Coupled Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1606.07536

EBGAN

Energy-based Generative Adversarial Network

• author: Junbo Zhao, Michael Mathieu, Yann LeCun
• arxiv: http://arxiv.org/abs/1609.03126
• github(Tensorflow): https://github.com/buriburisuri/ebgan

SeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

• arxiv: https://arxiv.org/abs/1609.05473
• github: https://github.com/LantaoYu/SeqGAN

Connecting Generative Adversarial Networks and Actor-Critic Methods

• arxiv: https://arxiv.org/abs/1610.01945

Generative Adversarial Nets from a Density Ratio Estimation Perspective

• arxiv: https://arxiv.org/abs/1610.02920

Unrolled Generative Adversarial Networks

• paper: http://openreview.net/pdf?id=BydrOIcle
• github: https://github.com/bstriner/keras-adversarial

Generative Adversarial Networks as Variational Training of Energy Based Models

• arxiv: https://arxiv.org/abs/1611.01799
• github: https://github.com/Shuangfei/vgan

Multi-class Generative Adversarial Networks with the L2 Loss Function

Least Squares Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1611.04076

Inverting The Generator Of A Generative Adversarial Networ

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1611.05644

ml4a-invisible-cities

• project page: https://opendot.github.io/ml4a-invisible-cities/
• arxiv: https://github.com/opendot/ml4a-invisible-cities

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1611.06430

Associative Adversarial Networks

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1611.06953

Temporal Generative Adversarial Nets

• arxiv: https://arxiv.org/abs/1611.06624

Handwriting Profiling using Generative Adversarial Networks

• intro: Accepted at The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17 Student Abstract and Poster Program)
• arxiv: https://arxiv.org/abs/1611.08789

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

• intro: Constructive Machine Learning Workshop (CML) at NIPS 2016
• project page: http://mogren.one/publications/2016/c-rnn-gan/
• arxiv: https://arxiv.org/abs/1611.09904
• github: https://github.com/olofmogren/c-rnn-gan

Ensembles of Generative Adversarial Networks

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1612.00991

Improved generator objectives for GANs

• intro: NIPS 2016 Workshop on Adversarial Training
• arxiv: https://arxiv.org/abs/1612.02780

SGAN

Stacked Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1612.04357
• github: https://github.com/xunhuang1995/SGAN

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

• intro: Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1612.05424

AdaGAN: Boosting Generative Models

• intro: Max Planck Institute for Intelligent Systems & Google Brain
• arxiv: https://arxiv.org/abs/1701.02386

Towards Principled Methods for Training Generative Adversarial Networks

• intro: Courant Institute of Mathematical Sciences & Facebook AI Research
• arxiv: https://arxiv.org/abs/1701.04862

Wasserstein GAN

• intro: Courant Institute of Mathematical Sciences & Facebook AI Research
• arxiv: https://arxiv.org/abs/1701.07875
• github: https://github.com/martinarjovsky/WassersteinGAN
• github: https://github.com/Zardinality/WGAN-tensorflow
• github(Tensorflow/Keras): https://github.com/kuleshov/tf-wgan
• github: https://github.com/shekkizh/WassersteinGAN.tensorflow
• gist: https://gist.github.com/soumith/71995cecc5b99cda38106ad64503cee3
• reddit: https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/

Improved Training of Wasserstein GANs

• arxiv: https://arxiv.org/abs/1704.00028
• github: https://github.com/igul222/improved_wgan_training

On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

• arxiv: https://arxiv.org/abs/1704.03971
• github: https://github.com/stormraiser/GAN-weight-norm

Image-to-Image Translation

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

• project page: https://phillipi.github.io/pix2pix/
• arxiv: https://arxiv.org/abs/1611.07004
• github: https://github.com/phillipi/pix2pix
• github(TensorFlow): https://github.com/yenchenlin/pix2pix-tensorflow
• github(Chainer): https://github.com/mattya/chainer-pix2pix
• github(PyTorch): https://github.com/mrzhu-cool/pix2pix-pytorch
• github(Chainer): https://github.com/wuhuikai/chainer-pix2pix

Remastering Classic Films in Tensorflow with Pix2Pix

• blog: https://hackernoon.com/remastering-classic-films-in-tensorflow-with-pix2pix-f4d551fa0503#.6dmahnt8n
• github: https://github.com/awjuliani/Pix2Pix-Film
• model: https://drive.google.com/file/d/0B8x0IeJAaBccNFVQMkQ0QW15TjQ/view

Image-to-Image Translation in Tensorflow

• blog: http://affinelayer.com/pix2pix/index.html
• github: https://github.com/affinelayer/pix2pix-tensorflow

webcam pix2pix

https://github.com/memo/webcam-pix2pix-tensorflow

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

• intro: Imperial College London & Indian Institute of Technology
• arxiv: https://arxiv.org/abs/1701.02676

Unsupervised Image-to-Image Translation Networks

https://arxiv.org/abs/1703.00848

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

• arxiv: https://arxiv.org/abs/1703.10593
• github(Chainer): https://github.com/Aixile/chainer-cyclegan

CycleGAN and pix2pix in PyTorch

• intro: Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more)
• github: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

Perceptual Adversarial Networks for Image-to-Image Transformation

https://arxiv.org/abs/1706.09138

Projects

Generative Adversarial Networks with Keras

• github: https://github.com/phreeza/keras-GAN

Generative Adversarial Network Demo for Fresh Machine Learning #2

• youtube: https://www.youtube.com/watch?v=deyOX6Mt_As&feature=em-uploademail
• github: https://github.com/llSourcell/Generative-Adversarial-Network-Demo
• demo: http://cs.stanford.edu/people/karpathy/gan/

TextGAN: A generative adversarial network for text generation, written in TensorFlow.

• github: https://github.com/AustinStoneProjects/TextGAN

cleverhans v0.1: an adversarial machine learning library

• arxiv: https://arxiv.org/abs/1610.00768
• github: https://github.com/openai/cleverhans

Deep Convolutional Variational Autoencoder w/ Adversarial Network

• intro: An implementation of the deep convolutional generative adversarial network, combined with a varational autoencoder
• github: https://github.com/staturecrane/dcgan_vae_torch

A versatile GAN(generative adversarial network) implementation. Focused on scalability and ease-of-use.

• github: https://github.com/255BITS/HyperGAN

Blogs

Generative Adversial Networks Explained

• blog: http://kvfrans.com/generative-adversial-networks-explained/

Generative Adversarial Autoencoders in Theano

• blog: https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/
• github: https://github.com/mikesj-public/dcgan-autoencoder

An introduction to Generative Adversarial Networks (with code in TensorFlow)

• blog: http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
• github: https://github.com/AYLIEN/gan-intro

Difficulties training a Generative Adversarial Network

• github: https://github.com/shekkizh/neuralnetworks.thought-experiments/blob/master/Generative%20Models/GAN/Readme.md

Are Energy-Based GANs any more energy-based than normal GANs?

http://www.inference.vc/are-energy-based-gans-actually-energy-based/

Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode: Plus a Tensorflow tutorial for implementing your own GAN

• blog: https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39#.rpiunhdjh
• gist: https://gist.github.com/awjuliani/8ebf356d03ffee139659807be7fa2611

Deep Learning Research Review Week 1: Generative Adversarial Nets

• blog: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-1-Generative-Adversarial-Nets

Stability of Generative Adversarial Networks

• blog: http://www.araya.org/archives/1183

Instance Noise: A trick for stabilising GAN training

• blog: http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/

Generating Fine Art in 300 Lines of Code

• intro: DCGAN
• blog: https://medium.com/@richardherbert/generating-fine-art-in-300-lines-of-code-4d37218216a6#.63qm8ef9g

Talks / Videos

Generative Adversarial Network visualization

• youtube: https://www.youtube.com/watch?v=mObnwR-u8pc

Resources

AdversarialNetsPapers: The classical Papers about adversial nets

• github: https://github.com/zhangqianhui/AdversarialNetsPapers