Generative Adversarial Networks

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

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

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

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

• intro: DCGAN
• 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

Energy-based Generative Adversarial Network

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

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

• 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

Stacked Generative Adversarial Networks

• intro: SGAN
• 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

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1704.00028
• github(TensorFlow): https://github.com/igul222/improved_wgan_training
• github: https://github.com/jalola/improved-wgan-pytorch

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

Controllable Generative Adversarial Network

• intro: Korea University
• arxiv: https://arxiv.org/abs/1708.00598

Generative Adversarial Networks: An Overview

• intro: Imperial College London & Victoria University of Wellington & University of Montreal & Cortexica Vision Systems Ltd
• intro: IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding
• arxiv: https://arxiv.org/abs/1710.07035

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

https://arxiv.org/abs/1711.03213

Spectral Normalization for Generative Adversarial Networks

https://openreview.net/forum?id=B1QRgziT-

Are GANs Created Equal? A Large-Scale Study

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1711.10337
• reddit: https://www.reddit.com/r/MachineLearning/comments/7gwip3/d_googles_large_scale_gantuning_paper_unfairly/

GAGAN: Geometry-Aware Generative Adverserial Networks

https://arxiv.org/abs/1712.00684

CycleGAN: a Master of Steganography

• intro: NIPS 2017, workshop on Machine Deception
• arxiv: https://arxiv.org/abs/1712.02950

PacGAN: The power of two samples in generative adversarial networks

• intro: CMU & University of Illinois at Urbana-Champaign
• arxiv: https://arxiv.org/abs/1712.04086

ComboGAN: Unrestrained Scalability for Image Domain Translation

• arxiv: https://arxiv.org/abs/1712.06909
• github: https://github.com/AAnoosheh/ComboGAN

Decoupled Learning for Conditional Adversarial Networks

https://arxiv.org/abs/1801.06790

No Modes left behind: Capturing the data distribution effectively using GANs

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1802.00771

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

• intro: ICLR 2018
• arxiv: https://arxiv.org/abs/1805.03644
• github: https://github.com/BorealisAI/bre-gan

On GANs and GMMs

https://arxiv.org/abs/1805.12462

The Unusual Effectiveness of Averaging in GAN Training

https://arxiv.org/abs/1806.04498

Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis

https://arxiv.org/abs/1807.00751

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1807.04720
• github: https://github.com/google/compare_gan

Which Training Methods for GANs do actually Converge?

• intro: ICML 2018. MPI Tübingen & Microsoft Research
• project page: https://avg.is.tuebingen.mpg.de/publications/meschedericml2018
• paper: https://avg.is.tuebingen.mpg.de/uploads_file/attachment/attachment/424/Mescheder2018ICML.pdf
• github: https://github.com/LMescheder/GAN_stability

Convergence Problems with Generative Adversarial Networks (GANs)

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1806.11382

Bayesian CycleGAN via Marginalizing Latent Sampling

https://arxiv.org/abs/1811.07465

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

https://arxiv.org/abs/1811.10597

Do GAN Loss Functions Really Matter?

https://arxiv.org/abs/1811.09567

Image-to-Image Translation

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

• intro: CVPR 2017
• 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

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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

• intro: NIPS 2017 Spotlight
• intro: unsupervised/unpaired image-to-image translation using coupled GANs
• project page: http://research.nvidia.com/publication/2017-12_Unsupervised-Image-to-Image-Translation
• arxiv: https://arxiv.org/abs/1703.00848
• github: https://github.com/mingyuliutw/UNIT

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

• intro: UC Berkeley
• project page: https://junyanz.github.io/CycleGAN/
• arxiv: https://arxiv.org/abs/1703.10593
• github(official, Torch): https://github.com/junyanz/CycleGAN
• github(official, PyTorch): https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
• github(PyTorch): https://github.com/eveningglow/semi-supervised-CycleGAN
• 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

XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

• intro: IST Austria & Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1711.05139

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

https://arxiv.org/abs/1711.09334

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

• intro: Korea University & Clova AI Research
• arxiv: https://arxiv.org/abs/1711.09020
• github: https://github.com//yunjey/StarGAN

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

https://arxiv.org/abs/1711.09554

Toward Multimodal Image-to-Image Translation

• intro: NIPS 2017. BicycleGAN
• project page: https://junyanz.github.io/BicycleGAN/
• arxiv: https://arxiv.org/abs/1711.11586
• github(official, PyTorch): https://github.com//junyanz/BicycleGAN
• github: https://github.com/gitlimlab/BicycleGAN-Tensorflow
• github: https://github.com/kvmanohar22/img2imgGAN
• github: https://github.com/eveningglow/BicycleGAN-pytorch

Face Translation between Images and Videos using Identity-aware CycleGAN

https://arxiv.org/abs/1712.00971

Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders

https://arxiv.org/abs/1712.02050

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

• intro: NVIDIA Corporation, UC Berkeley
• project page: https://tcwang0509.github.io/pix2pixHD/
• arxiv: https://arxiv.org/abs/1711.11585
• github: https://github.com/NVIDIA/pix2pixHD
• youtube: https://www.youtube.com/watch?v=3AIpPlzM_qs&feature=youtu.be

On the Effectiveness of Least Squares Generative Adversarial Networks

https://arxiv.org/abs/1712.06391

GANs for Limited Labeled Data

• intro: Ian Goodfellow
• slides: http://www.iangoodfellow.com/slides/2017-12-09-label.pdf

Defending Against Adversarial Examples

• intro: Ian Goodfellow
• slides: http://www.iangoodfellow.com/slides/2017-12-08-defending.pdf

Conditional Image-to-Image Translation

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1805.00251

XOGAN: One-to-Many Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.07277

Unsupervised Attention-guided Image to Image Translation

https://arxiv.org/abs/1806.02311

Exemplar Guided Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1805.11145

Improving Shape Deformation in Unsupervised Image-to-Image Translation

https://arxiv.org/abs/1808.04325

Video-to-Video Synthesis

• arxiv: https://arxiv.org/abs/1808.06601
• github: https://github.com/NVIDIA/vid2vid

Segmentation Guided Image-to-Image Translation with Adversarial Networks

https://arxiv.org/abs/1901.01569

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

AdaGAN: Boosting Generative Models

• intro: AdaGAN: greedy iterative procedure to train mixtures of GANs
• intro: Max Planck Institute for Intelligent Systems & Google Brain
• arxiv: https://arxiv.org/abs/1701.02386
• github: https://github.com/tolstikhin/adagan

TensorFlow-GAN (TFGAN)

• intro: TFGAN: A Lightweight Library for Generative Adversarial Networks
• github: https://github.com//tensorflow/tensorflow/tree/master/tensorflow/contrib/gan
• blog: https://research.googleblog.com/2017/12/tfgan-lightweight-library-for.html

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

The GAN Zoo

• intro: A list of all named GANs!
• github: https://github.com/hindupuravinash/the-gan-zoo

AdversarialNetsPapers: The classical Papers about adversial nets

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

GAN Timeline

• intro: A timeline showing the development of Generative Adversarial Networks (GAN)
• github: https://github.com//dongb5/GAN-Timeline