Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/monitor1379/generativeadversarialnetspapers

Papers, codes, slides and blogs about Generative Adversrial Nets.
https://github.com/monitor1379/generativeadversarialnetspapers

generative-adversarial-networks paper

Last synced: 6 months ago
JSON representation

Papers, codes, slides and blogs about Generative Adversrial Nets.

Awesome Lists containing this project

README 

          
# GenerativeAdversarialNetsPapers

Papers, codes, slides and blogs about Generative Adversrial Nets.



# 1. Papers 



## 1.1 The First paper

:white_check_mark: [Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661)

[[Code]](https://github.com/goodfeli/adversarial)(the first paper about it)



## 1.2 Unclassified



:white_medium_square: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1511.06390.pdf)



:white_medium_square: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [[Paper]](https://arxiv.org/abs/1506.05751)[[Code]](https://github.com/facebook/eyescream)



:white_medium_square: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1511.06434)[[Code]](https://github.com/jacobgil/keras-dcgan)(Gan with convolutional networks)(ICLR)



:white_medium_square: [Adversarial Autoencoders] [[Paper]](http://arxiv.org/abs/1511.05644)[[Code]](https://github.com/musyoku/adversarial-autoencoder)



:white_medium_square: [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [[Paper]](https://arxiv.org/pdf/1602.02644v2.pdf)



:white_medium_square: [Generating images with recurrent adversarial networks] [[Paper]](https://arxiv.org/abs/1602.05110)[[Code]](https://github.com/ofirnachum/sequence_gan)



:white_medium_square: [Generative Visual Manipulation on the Natural Image Manifold] [[Paper]](https://people.eecs.berkeley.edu/~junyanz/projects/gvm/eccv16_gvm.pdf)[[Code]](https://github.com/junyanz/iGAN)



:white_medium_square: [Generative Adversarial Text to Image Synthesis] [[Paper]](https://arxiv.org/abs/1605.05396)[[Code]](https://github.com/reedscot/icml2016)[[code]](https://github.com/paarthneekhara/text-to-image)



:white_medium_square: [Learning What and Where to Draw] [[Paper]](http://www.scottreed.info/files/nips2016.pdf)[[Code]](https://github.com/reedscot/nips2016)



:white_medium_square: [Adversarial Training for Sketch Retrieval] [[Paper]](http://link.springer.com/chapter/10.1007/978-3-319-46604-0_55)



:white_medium_square: [Generative Image Modeling using Style and Structure Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1603.05631.pdf)[[Code]](https://github.com/xiaolonw/ss-gan)



:white_medium_square: [Generative Adversarial Networks as Variational Training of Energy Based Models] [[Paper]](http://www.mathpubs.com/detail/1611.01799v1/Generative-Adversarial-Networks-as-Variational-Training-of-Energy-Based-Models)(ICLR 2017)



:white_medium_square: [Adversarial Training Methods for Semi-Supervised Text Classification] [[Paper]](https://arxiv.org/abs/1605.07725)[[Note]](https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md)( Ian Goodfellow Paper)



:white_medium_square: [Learning from Simulated and Unsupervised Images through Adversarial Training] [[Paper]](https://arxiv.org/abs/1612.07828)[[code]](https://github.com/carpedm20/simulated-unsupervised-tensorflow)（Apple paper）



:white_medium_square: [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [[Paper]](https://arxiv.org/pdf/1605.09304v5.pdf)[[Code]](https://github.com/Evolving-AI-Lab/synthesizing)



:white_medium_square: [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.01081)[[Code]](https://github.com/imatge-upc/saliency-salgan-2017)



:white_medium_square: [Adversarial Feature Learning] [[Paper]](https://arxiv.org/abs/1605.09782)



## 1.3 Ensemble 



:white_medium_square: [AdaGAN: Boosting Generative Models] [[Paper]](https://arxiv.org/abs/1701.02386)[[Code]]（Google Brain）



## 1.4 Image Inpainting



:white_medium_square: [Semantic Image Inpainting with Perceptual and Contextual Losses] [[Paper]](https://arxiv.org/abs/1607.07539)[[Code]](https://github.com/bamos/dcgan-completion.tensorflow)



:white_medium_square: [Context Encoders: Feature Learning by Inpainting] [[Paper]](https://arxiv.org/abs/1604.07379)[[Code]](https://github.com/jazzsaxmafia/Inpainting)



:white_medium_square: [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.06430v1)



## 1.5 Super-Resolution



:white_medium_square: [Image super-resolution through deep learning ][[Code]](https://github.com/david-gpu/srez)(Just for face dataset)



:white_medium_square: [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [[Paper]](https://arxiv.org/abs/1609.04802)[[Code]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)（Using Deep residual network）



:white_medium_square: [EnhanceGAN] [[Docs]](https://medium.com/@richardherbert/faces-from-noise-super-enhancing-8x8-images-with-enhancegan-ebda015bb5e0#.io6pskvin)[[Code]]



## 1.6 Disocclusion



:white_medium_square: [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [[Paper]](https://arxiv.org/abs/1612.08534)



## 1.7 Semantic Segmentation



:white_medium_square: [Semantic Segmentation using Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.08408)（soumith's paper）



## 1.8 Object Detection



:white_medium_square: [Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted）



## 1.9 RNN



:white_medium_square: [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [[Paper]](https://arxiv.org/abs/1611.09904)[[Code]](https://github.com/olofmogren/c-rnn-gan)



## 1.10 Conditional adversarial



:white_medium_square: [Conditional Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1411.1784)[[Code]](https://github.com/zhangqianhui/Conditional-Gans)



:white_medium_square: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1606.03657)[[Code]](https://github.com/buriburisuri/supervised_infogan)



:white_medium_square: [Image-to-image translation using conditional adversarial nets] [[Paper]](https://arxiv.org/pdf/1611.07004v1.pdf)[[Code]](https://github.com/phillipi/pix2pix)[[Code]](https://github.com/yenchenlin/pix2pix-tensorflow)



:white_medium_square: [Conditional Image Synthesis With Auxiliary Classifier GANs] [[Paper]](https://arxiv.org/abs/1610.09585)[[Code]](https://github.com/buriburisuri/ac-gan)(GoogleBrain ICLR 2017)



:white_medium_square: [Pixel-Level Domain Transfer] [[Paper]](https://arxiv.org/pdf/1603.07442v2.pdf)[[Code]](https://github.com/fxia22/pldtgan)



:white_medium_square: [Invertible Conditional GANs for image editing] [[Paper]](https://arxiv.org/abs/1611.06355)[[Code]](https://github.com/Guim3/IcGAN)



:white_medium_square: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [[Paper]](https://arxiv.org/abs/1612.00005v1)[[Code]](https://github.com/Evolving-AI-Lab/ppgn)



:white_medium_square: [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1612.03242v1.pdf)[[Code]](https://github.com/hanzhanggit/StackGAN)



:white_medium_square: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1701.02676.pdf)



## 1.11 Video Prediction



:white_medium_square: [Deep multi-scale video prediction beyond mean square error] [[Paper]](https://arxiv.org/abs/1511.05440)[[Code]](https://github.com/dyelax/Adversarial_Video_Generation)(Yann LeCun's paper)



:white_medium_square: [Unsupervised Learning for Physical Interaction through Video Prediction] [[Paper]](https://arxiv.org/abs/1605.07157)(Ian Goodfellow's paper)



:white_medium_square: [Generating Videos with Scene Dynamics] [[Paper]](https://arxiv.org/abs/1609.02612)[[Web]](http://web.mit.edu/vondrick/tinyvideo/)[[Code]](https://github.com/cvondrick/videogan)



##Texture Synthesis & style transfer



:white_medium_square: [Precomputed real-time texture synthesis with markovian generative adversarial networks] [[Paper]](https://arxiv.org/abs/1604.04382)[[Code]](https://github.com/chuanli11/MGANs)(ECCV 2016)



## 1.12 GAN Theory



:white_medium_square: [Energy-based generative adversarial network] [[Paper]](https://arxiv.org/pdf/1609.03126v2.pdf)[[Code]](https://github.com/buriburisuri/ebgan)(Lecun paper)



:white_medium_square: [Improved Techniques for Training GANs] [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper)



:white_medium_square: [Mode RegularizedGenerative Adversarial Networks] [[Paper]](https://openreview.net/pdf?id=HJKkY35le)(Yoshua Bengio , ICLR 2017)



:white_medium_square: [Improving Generative Adversarial Networks with Denoising Feature Matching] [[Paper]](https://openreview.net/pdf?id=S1X7nhsxl)[[Code]](https://github.com/hvy/chainer-gan-denoising-feature-matching)(Yoshua Bengio , ICLR 2017)



:white_medium_square: [Sampling Generative Networks] [[Paper]](https://arxiv.org/abs/1609.04468)[[Code]](https://github.com/dribnet/plat)



:white_medium_square: [Mode Regularized Generative Adversarial Networkss] [[Paper]](https://arxiv.org/abs/1612.02136)( Yoshua Bengio's paper)



:white_medium_square: [How to train Gans] [[Docu]](https://github.com/soumith/ganhacks#authors)



:white_medium_square: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](http://openreview.net/forum?id=Hk4_qw5xe)(ICLR 2017)



:white_medium_square: [Unrolled Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.02163)[[Code]](https://github.com/poolio/unrolled_gan)



:white_check_mark: [Wasserstein GAN] [[Paper]](https://arxiv.org/abs/1701.07875)[[Code]](https://github.com/martinarjovsky/WassersteinGAN)



:white_medium_square: [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities] [[Paper]](https://arxiv.org/abs/1701.06264)[[Code]](https://github.com/guojunq/lsgan)(The same as WGan)



:white_medium_square: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.04862)



## 1.13 3D 



:white_medium_square: [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [[Paper]](https://arxiv.org/abs/1610.07584)[[Web]](http://3dgan.csail.mit.edu/)[[code]](https://github.com/zck119/3dgan-release)(2016 NIPS)



##Face Generative and Editing



:white_medium_square: [Autoencoding beyond pixels using a learned similarity metric] [[Paper]](https://arxiv.org/abs/1512.09300)[[code]](https://github.com/andersbll/autoencoding_beyond_pixels)



:white_medium_square: [Coupled Generative Adversarial Networks] [[Paper]](http://mingyuliu.net/)[[Caffe Code]](https://github.com/mingyuliutw/CoGAN)[[Tensorflow Code]](https://github.com/andrewliao11/CoGAN-tensorflow)（NIPS）



:white_medium_square: [Invertible Conditional GANs for image editing] [[Paper]](https://drive.google.com/file/d/0B48XS5sLi1OlRkRIbkZWUmdoQmM/view)[[Code]](https://github.com/Guim3/IcGAN)



:white_medium_square: [Learning Residual Images for Face Attribute Manipulation] [[Paper]](https://arxiv.org/abs/1612.05363)



:white_medium_square: [Neural Photo Editing with Introspective Adversarial Networks] [[Paper]](https://arxiv.org/abs/1609.07093)[[Code]](https://github.com/ajbrock/Neural-Photo-Editor)(ICLR 2017)



## 1.14 For discrete distributions



:white_medium_square: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1702.07983v1)



# 2. Project 



:white_medium_square: [cleverhans] [[Code]](https://github.com/openai/cleverhans)(A library for benchmarking vulnerability to adversarial examples)



:white_medium_square: [reset-cppn-gan-tensorflow] [[Code]](https://github.com/hardmaru/resnet-cppn-gan-tensorflow)(Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)



:white_medium_square: [HyperGAN] [[Code]](https://github.com/255bits/HyperGAN)(Open source GAN focused on scale and usability)



# 3. Blogs



| Author | Address |

|---- | ---|----|

| **inFERENCe** |  [Adversarial network](http://www.inference.vc/)  |

| **inFERENCe** |  [InfoGan](http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/)  |

| **distill** |  [Deconvolution and Image Generation](http://distill.pub/2016/deconv-checkerboard/)  |

| **yingzhenli** |  [Gan theory](http://www.yingzhenli.net/home/blog/?p=421http://www.yingzhenli.net/home/blog/?p=421)  |

| **OpenAI** |  [Generative model](https://openai.com/blog/generative-models/)  |



# 4. Other



:white_medium_square: [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[[Chinese Trans]](http://c.m.163.com/news/a/C7UE2MLT0511AQHO.html?spss=newsapp&spsw=1)[[details]](https://arxiv.org/pdf/1701.00160v1.pdf)



:white_medium_square: [2] [[PDF]](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view)(NIPS Lecun Slides)



# 5. Adversarial Examples



| Title | Paper | Code |

|---- | ---|----|----|

| **Intriguing properties of neural networks** |  [Paper](http://arxiv.org/abs/1312.6199)  |[Code]|

| **Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images** |  [Paper](https://arxiv.org/abs/1412.1897)  |[Code]|

| **Explaining and Harnessing Adversarial Examples** |  [Paper](http://arxiv.org/abs/1412.6572)  |[Code]|

| **Adversarial examples in the physical world** |  [Paper](http://arxiv.org/abs/1607.02533)  |[Code]|

| **Universal adversarial perturbations** |  [Paper](https://arxiv.org/abs/1610.08401)  |[Code]|

| **Robustness of classifiers: from adversarial to random noise** |  [Paper](https://arxiv.org/abs/1608.08967)  |[Code]|

| **DeepFool: a simple and accurate method to fool deep neural networks** |  [Paper](https://arxiv.org/abs/1511.04599)  |[Code]|

| **Goodfellow Slides** |  [Paper](http://www.iangoodfellow.com/slides/2016-12-9-AT.pdf)  |[Code]|

| **The Limitations of Deep Learning in Adversarial Settings** |  [Paper](https://arxiv.org/abs/1511.07528)  |[Code](https://github.com/openai/cleverhans/blob/master/tutorials/mnist_tutorial_jsma.md)|

| **Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples** |  [Paper](https://arxiv.org/abs/1602.02697)  |[Code]|



# 6. Timeline(**TODO**)



2014 GAN 《Generative Adversarial Networks》-Ian Goodfellow, arXiv:1406.2661v1 



2014 CGAN 《Conditional Generative Adversarial Nets》- Mehdi Mirza, arXiv:1411.1784v1 



2015 LAPGAN 《Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks》- Emily Denton & Soumith Chintala, arxiv: 1506.05751 



2015 SRGAN《super-resolution generative adversarial network》- Joan Bruna, Pablo Sprechmann, Yann LeCun , arXiv:1511.05666 



2015《Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks》- Jost Tobias Springenberg ,arXiv:1511.06390 



2015 DCGAN《Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks》 - Alec Radford & Luke Metz, arxiv:1511.06434 



2015 VAEGAN 《Autoencoding beyond pixels using a learned similarity metric》 - Anders Boesen Lindbo Larsen, arxiv: 1512.09300 



2016《Generating Images with Recurrent Adversarial Networks》- Daniel Jiwoong Im, Chris Dongjoo Kim ,arXiv:1602.05110 



2016《Generative Adversarial Text to Image Synthesis》（“GANs 文字到图像的合成”）- Scott Reed ，arXiv:1605.05396 



2016 InfoGAN《InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsI》- Xi Chen, arxiv: 1606.03657 



2016 COGAN《Coupled Generative Adversarial Networks》Ming-Yu Liu, Oncel Tuzel - arXiv:1606.07536 



2016 EBGAN《Energy-based Generative Adversarial Network》- Junbo Zhao ， arXiv:1609.03126v2 



2016 《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》 - Christian Ledig, Lucas Theis , arXiv:1609.04802 



2016 SeqGAN《SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient》- Lantao Yu, arxiv: 1609.05473 



2016《 Contextual RNN-GANs for Abstract Reasoning Diagram Generation》 - Arnab Ghosh, Viveka Kulharia ,arXiv:1609.09444 



2016《Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling》- Jiajun Wu, Chengkai Zhang ,arXiv:1610.07584 



2016 TGAN《Temporal Generative Adversarial Nets》- Masaki Saito, Eiichi Matsumoto，arXiv:1611.06624 



2016 SAD-GAN《SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks》- Arna Ghosh, Biswarup Bhattacharya, Somnath Basu Roy Chowdhury ，arXiv:1611.08788 



2016 PPGAN 《Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space》 - Anh Nguyen , arXiv:1612.00005v1 



2016 《StackGAN:Text to Photo realistic Image Synthesis with Stacked Generative Adversarial Network》- Han Zhang,arXiv:1612.03242 



2017 《NIPS 2016 Tutorial: Generative Adversarial Networks 》- Ian Goodfellow , arXiv:1701.00160 



2017 LS-GAN《 Loss-Sensitive Generative Adversarial Networks onLipschitz Densities》- Guo-Jun Qi ，arXiv:1701.06264 



2017 WGAN 《Wasserstein GAN》- Martin Arjovsky ,arXiv:1701.07875v1 



2017《Maximum-Likelihood Augmented Discrete Generative Adversarial Networks》-Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio，arXiv:1702.07983v1 



2017《Boundary-Seeking Generative Adversarial Networks》- R Devon Hjelm, Athul Paul Jacob, Tong Che, Kyunghyun Cho, Yoshua Bengio ,arXiv:1702.08431 



2017《Mode Regularized Generative Adversarial Networks》- Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li， ICLR 2017



2017《 Adversarial examples for generative models》- Jernej Kos, Ian Fischer, Dawn Song ， arXiv:1702.06832 



2017《 Learning to Draw Dynamic Agent Goals with Generative Adversarial Networks》- Shariq Iqbal, John Pearson ，arXiv:1702.07319 



2017 《WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images》- Jie Li, Katherine A. Skinner, Ryan M. Eustice, Matthew Johnson-Roberson ，arXiv:1702.07392 



2017《Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning》- Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz ，arXiv:1702.07464 



2017 《Generative Adversarial Active Learning》- Jia-Jie Zhu, José Bento ，arXiv:1702.07956 



2017 《Maximum-Likelihood Augmented Discrete Generative Adversarial Networks》 

- Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio ， arXiv:1702.07983 



2017 《 Adversarial Networks for the Detection of Aggressive Prostate Cancer》- 

Simon Kohl, David Bonekamp, arXiv:1702.08014 



2017《McGan: Mean and Covariance Feature Matching GAN》- Youssef Mroueh, Tom Sercu, Vaibhava Goel ，arXiv:1702.08398 



2017 《 Age Progression/Regression by Conditional Adversarial Autoencoder》- 

Zhifei Zhang, Yang Song, Hairong Qi ，arXiv:1702.08423 



2017 《ste-GAN-ography: Generating Steganographic Images via Adversarial Training 》- Jamie Hayes, George Danezis， arXiv:1703.00371 



2017 《Generalization and Equilibrium in Generative Adversarial Nets (GANs) 》- Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang， arXiv:1703.00573



# Author



[@monitor1379](https://github.com/monitor1379)