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
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
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
Connecting Generative Adversarial Networks and Actor-Critic Methods
Generative Adversarial Nets from a Density Ratio Estimation Perspective
Unrolled Generative Adversarial Networks
Generative Adversarial Networks as Variational Training of Energy Based Models
Multi-class Generative Adversarial Networks with the L2 Loss Function
Least Squares Generative Adversarial Networks
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
Associative Adversarial Networks
- intro: NIPS 2016 Workshop on Adversarial Training
- arxiv: https://arxiv.org/abs/1611.06953
Temporal Generative Adversarial Nets
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
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
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
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
Segmentation Guided Image-to-Image Translation with Adversarial Networks
https://arxiv.org/abs/1901.01569
ForkGAN: Seeing into the rainy night
- intro: ECCV 2020 oral
- intro: UISEE Technology & Kyoto University & University of Pennsylvania
- paper: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480154.pdf
- github: https://github.com/zhengziqiang/ForkGAN
- presentation: https://www.youtube.com/watch?v=O2nxRsSwkzs&t=1s
Projects
Generative Adversarial Networks with Keras
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.
cleverhans v0.1: an adversarial machine learning library
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.
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
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
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
Stability of Generative Adversarial Networks
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
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
GAN Timeline
- intro: A timeline showing the development of Generative Adversarial Networks (GAN)
- github: https://github.com//dongb5/GAN-Timeline