# AdversarialNetsPapers **Repository Path**: AttackerGry/AdversarialNetsPapers ## Basic Information - **Project Name**: AdversarialNetsPapers - **Description**: 关于生成对抗网的代码的经典论文列表 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-07-22 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # AdversarialNetsPapers The classic about Generative Adversarial Networks First paper -------------------------------------------- :heavy_check_mark: [Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661) [[Code]](https://github.com/goodfeli/adversarial)(the First paper of GAN) ## Unclassified :heavy_check_mark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [[Paper]](https://arxiv.org/abs/1506.05751)[[Code]](https://github.com/facebook/eyescream) :heavy_check_mark: [Adversarial Autoencoders] [[Paper]](http://arxiv.org/abs/1511.05644)[[Code]](https://github.com/musyoku/adversarial-autoencoder) :heavy_check_mark: [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [[Paper]](https://arxiv.org/pdf/1602.02644v2.pdf) :heavy_check_mark: [Generating images with recurrent adversarial networks] [[Paper]](https://arxiv.org/abs/1602.05110)[[Code]](https://github.com/ofirnachum/sequence_gan) :heavy_check_mark: [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) :heavy_check_mark: [Learning What and Where to Draw] [[Paper]](http://www.scottreed.info/files/nips2016.pdf)[[Code]](https://github.com/reedscot/nips2016) :heavy_check_mark: [Adversarial Training for Sketch Retrieval] [[Paper]](http://link.springer.com/chapter/10.1007/978-3-319-46604-0_55) :heavy_check_mark: [Generative Image Modeling using Style and Structure Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1603.05631.pdf)[[Code]](https://github.com/xiaolonw/ss-gan) :heavy_check_mark: [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) :heavy_check_mark: [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) :heavy_check_mark: [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.01081)[[Code]](https://github.com/imatge-upc/saliency-salgan-2017) :heavy_check_mark: [Adversarial Feature Learning] [[Paper]](https://arxiv.org/abs/1605.09782) :heavy_check_mark: [Adversarially Learned Inference][[Paper]](https://arxiv.org/abs/1606.00704)[[Code]](https://github.com/IshmaelBelghazi/ALI) ## GAN Theory :heavy_check_mark: [Energy-based generative adversarial network] [[Paper]](https://arxiv.org/pdf/1609.03126v2.pdf)[[Code]](https://github.com/buriburisuri/ebgan)(Lecun paper) :heavy_check_mark: [Improved Techniques for Training GANs] [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper) :heavy_check_mark: [Mode Regularized Generative Adversarial Networks] [[Paper]](https://openreview.net/pdf?id=HJKkY35le)(Yoshua Bengio , ICLR 2017) :heavy_check_mark: [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) :heavy_check_mark: [Sampling Generative Networks] [[Paper]](https://arxiv.org/abs/1609.04468)[[Code]](https://github.com/dribnet/plat) :heavy_check_mark: [How to train Gans] [[Docu]](https://github.com/soumith/ganhacks#authors) :heavy_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](http://openreview.net/forum?id=Hk4_qw5xe)(ICLR 2017) :heavy_check_mark: [Unrolled Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.02163)[[Code]](https://github.com/poolio/unrolled_gan)(ICLR 2017) :heavy_check_mark: [Least Squares Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.04076)[[Code]](https://github.com/pfnet-research/chainer-LSGAN)(ICCV 2017) :heavy_check_mark: [Wasserstein GAN] [[Paper]](https://arxiv.org/abs/1701.07875)[[Code]](https://github.com/martinarjovsky/WassersteinGAN) :heavy_check_mark: [Improved Training of Wasserstein GANs] [[Paper]](https://arxiv.org/abs/1704.00028)[[Code]](https://github.com/igul222/improved_wgan_training)(The improve of wgan) :heavy_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1701.04862) :heavy_check_mark: [Generalization and Equilibrium in Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1703.00573)（ICML 2017） :heavy_check_mark: [Spectral Normalization for Generative Adversarial Networks][[Paper]](https://openreview.net/forum?id=B1QRgziT-)[[code]](https://github.com/minhnhat93/tf-SNDCGAN)（ICLR 2018） ## Generation High-Quality Images :heavy_check_mark: [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) :heavy_check_mark: [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) :heavy_check_mark: [Improved Techniques for Training GANs] [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper) :heavy_check_mark: [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) :heavy_check_mark: [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) :heavy_check_mark: [Improved Training of Wasserstein GANs] [[Paper]](https://arxiv.org/abs/1704.00028)[[Code]](https://github.com/igul222/improved_wgan_training) :heavy_check_mark: [Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow] [[Paper]](https://arxiv.org/abs/1703.10717)[[Code]](https://github.com/artcg/BEGAN) :heavy_check_mark: [Progressive Growing of GANs for Improved Quality, Stability, and Variation] [[Paper]](http://research.nvidia.com/publication/2017-10_Progressive-Growing-of)[[Code]](https://github.com/tkarras/progressive_growing_of_gans)[[Tensorflow Code]](https://github.com/zhangqianhui/PGGAN-tensorflow) :heavy_check_mark: [ Self-Attention Generative Adversarial Networks ] [[Paper]](https://arxiv.org/abs/1805.08318)[[Code]](https://github.com/heykeetae/Self-Attention-GAN) ## Semi-supervised learning :heavy_check_mark: [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) :heavy_check_mark: [Improved Techniques for Training GANs] [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper) :heavy_check_mark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1511.06390)(ICLR) :heavy_check_mark: [Semi-Supervised QA with Generative Domain-Adaptive Nets] [[Paper]](https://arxiv.org/abs/1702.02206)(ACL 2017) :heavy_check_mark: [Good Semi-supervised Learning that Requires a Bad GAN] [[Paper]](https://arxiv.org/abs/1705.09783)[[Code]](https://github.com/kimiyoung/ssl_bad_gan)(NIPS 2017) ## Ensemble :heavy_check_mark: [AdaGAN: Boosting Generative Models] [[Paper]](https://arxiv.org/abs/1701.02386)[[Code]]（Google Brain） ## Image blending :heavy_check_mark: [GP-GAN: Towards Realistic High-Resolution Image Blending] [[Paper]](https://arxiv.org/abs/1703.07195)[[Code]](https://github.com/wuhuikai/GP-GAN) ## Image Inpainting :heavy_check_mark: [Semantic Image Inpainting with Perceptual and Contextual Losses] [[Paper]](https://arxiv.org/abs/1607.07539)[[Code]](https://github.com/bamos/dcgan-completion.tensorflow)(CVPR 2017) :heavy_check_mark: [Context Encoders: Feature Learning by Inpainting] [[Paper]](https://arxiv.org/abs/1604.07379)[[Code]](https://github.com/jazzsaxmafia/Inpainting) :heavy_check_mark: [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.06430v1) :heavy_check_mark: [Generative face completion] [[Paper]](https://drive.google.com/file/d/0B8_MZ8a8aoSeenVrYkpCdnFRVms/edit)[[code]](https://github.com/Yijunmaverick/GenerativeFaceCompletion)(CVPR2017) :heavy_check_mark: [Globally and Locally Consistent Image Completion] [[MainPAGE]](http://hi.cs.waseda.ac.jp/~iizuka/projects/completion/en/)[[code]](https://github.com/satoshiiizuka/siggraph2017_inpainting)(SIGGRAPH 2017) :heavy_check_mark: [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis] [[Paper]](https://arxiv.org/abs/1611.09969)[[code]](https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting)(CVPR 2017) :heavy_check_mark: [Eye In-Painting with Exemplar Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1712.03999)[[Introduction]](https://github.com/bdol/exemplar_gans)[[Tensorflow code]](https://github.com/zhangqianhui/Exemplar_GAN_Eye_Inpainting)(CVPR2018) :heavy_check_mark: [Generative Image Inpainting with Contextual Attention] [[Paper]](https://arxiv.org/abs/1801.07892)[[Project]](http://jiahuiyu.com/deepfill)[[Demo]](http://jiahuiyu.com/deepfill)[[YouTube]](https://youtu.be/xz1ZvcdhgQ0)[[Code]](https://github.com/JiahuiYu/generative_inpainting)(CVPR2018) :heavy_check_mark: [Free-Form Image Inpainting with Gated Convolution] [[Paper]](https://arxiv.org/abs/1806.03589)[[Project]](http://jiahuiyu.com/deepfill2)[[YouTube]](https://youtu.be/uZkEi9Y2dj4) ## Super-Resolution :heavy_check_mark: [Image super-resolution through deep learning ][[Code]](https://github.com/david-gpu/srez)(Just for face dataset) :heavy_check_mark: [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） :heavy_check_mark: [EnhanceGAN] [[Docs]](https://medium.com/@richardherbert/faces-from-noise-super-enhancing-8x8-images-with-enhancegan-ebda015bb5e0#.io6pskvin)[[Code]] ## De-Occlusion :heavy_check_mark: [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [[Paper]](https://arxiv.org/abs/1612.08534) ## Semantic Segmentation :heavy_check_mark: [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [[Paper]](https://arxiv.org/abs/1612.05970)[[Code]](https://github.com/wentaozhu/adversarial-deep-structural-networks) :heavy_check_mark: [Semantic Segmentation using Adversarial Networks] [[Paper]](https://arxiv.org/abs/1611.08408)（soumith's paper） ## Object Detection :heavy_check_mark: [Perceptual generative adversarial networks for small object detection] [[Paper]](https://arxiv.org/abs/1706.05274v2)(CVPR 2017) :heavy_check_mark: [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [[Paper]](http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf)[[code]](https://github.com/xiaolonw/adversarial-frcnn)(CVPR2017) ## Conditional adversarial :heavy_check_mark: [Conditional Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1411.1784)[[Code]](https://github.com/zhangqianhui/Conditional-Gans) :heavy_check_mark: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1606.03657)[[Code]](https://github.com/buriburisuri/supervised_infogan)[[Code]](https://github.com/openai/InfoGAN) :heavy_check_mark: [Conditional Image Synthesis With Auxiliary Classifier GANs] [[Paper]](https://arxiv.org/abs/1610.09585)[[Code]](https://github.com/buriburisuri/ac-gan)(GoogleBrain ICLR 2017) :heavy_check_mark: [Pixel-Level Domain Transfer] [[Paper]](https://arxiv.org/pdf/1603.07442v2.pdf)[[Code]](https://github.com/fxia22/pldtgan) :heavy_check_mark: [Invertible Conditional GANs for image editing] [[Paper]](https://arxiv.org/abs/1611.06355)[[Code]](https://github.com/Guim3/IcGAN) :heavy_check_mark: [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) :heavy_check_mark: [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) ## Video Prediction and Generation :heavy_check_mark: [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) :heavy_check_mark: [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) :heavy_check_mark: [MoCoGAN: Decomposing Motion and Content for Video Generation] [[Paper]](https://arxiv.org/abs/1707.04993) ## Texture Synthesis & style transfer :heavy_check_mark: [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) # Image translation :heavy_check_mark: [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [[Paper]](https://arxiv.org/abs/1611.02200)[[Code]](https://github.com/yunjey/domain-transfer-network) :heavy_check_mark: [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) :heavy_check_mark: [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1703.05192)[[Code]](https://github.com/carpedm20/DiscoGAN-pytorch) :heavy_check_mark: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [[Paper]](https://junyanz.github.io/CycleGAN/)[[Code]](https://github.com/junyanz/CycleGAN) :heavy_check_mark: [CoGAN: Coupled Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1606.07536)[[Code]](https://github.com/andrewliao11/CoGAN-tensorflow)(NIPS 2016) :heavy_check_mark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [[Paper]](https://arxiv.org/pdf/1701.02676.pdf)(NIPS 2017) :heavy_check_mark: [Unsupervised Image-to-Image Translation Networks] [[Paper]](https://arxiv.org/abs/1703.00848) :heavy_check_mark: [Triangle Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1709.06548) :heavy_check_mark: [ST-GAN: Unsupervised Facial Image Semantic Transformation Using Generative Adversarial Networks] [[Paper]](http://proceedings.mlr.press/v77/zhang17c.html) :heavy_check_mark: [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] [[Paper]](https://arxiv.org/abs/1711.11585)[[code]](https://github.com/NVIDIA/pix2pixHD) :heavy_check_mark: [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings] [[Paper]](https://arxiv.org/abs/1711.05139)(Reviewed) :heavy_check_mark: [UNIT: UNsupervised Image-to-image Translation Networks] [[Paper]](https://arxiv.org/abs/1703.00848)[[Code]](https://github.com/mingyuliutw/UNIT)(NIPS 2017) :heavy_check_mark: [Toward Multimodal Image-to-Image Translation] [[Paper]](https://arxiv.org/abs/1711.11586)[[Code]](https://github.com/junyanz/BicycleGAN)(NIPS 2017) :heavy_check_mark: [Multimodal Unsupervised Image-to-Image Translation] [[Paper]](https://arxiv.org/abs/1804.04732)[[Code]](https://github.com/nvlabs/MUNIt) ## Facial Attribute Manipulation :heavy_check_mark: [Autoencoding beyond pixels using a learned similarity metric] [[Paper]](https://arxiv.org/abs/1512.09300)[[code]](https://github.com/andersbll/autoencoding_beyond_pixels)[[Tensorflow code]](https://github.com/zhangqianhui/vae-gan-tensorflow) :heavy_check_mark: [Coupled Generative Adversarial Networks] [[Paper]](http://mingyuliu.net/)[[Caffe Code]](https://github.com/mingyuliutw/CoGAN)[[Tensorflow Code]](https://github.com/andrewliao11/CoGAN-tensorflow)（NIPS） :heavy_check_mark: [Invertible Conditional GANs for image editing] [[Paper]](https://drive.google.com/file/d/0B48XS5sLi1OlRkRIbkZWUmdoQmM/view)[[Code]](https://github.com/Guim3/IcGAN) :heavy_check_mark: [Learning Residual Images for Face Attribute Manipulation] [[Paper]](https://arxiv.org/abs/1612.05363)[[code]](https://github.com/Zhongdao/FaceAttributeManipulation)(CVPR 2017) :heavy_check_mark: [Neural Photo Editing with Introspective Adversarial Networks] [[Paper]](https://arxiv.org/abs/1609.07093)[[Code]](https://github.com/ajbrock/Neural-Photo-Editor)(ICLR 2017) :heavy_check_mark: [Neural Face Editing with Intrinsic Image Disentangling] [[Paper]](https://arxiv.org/abs/1704.04131)(CVPR 2017) :heavy_check_mark: [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] [[Paper]](https://arxiv.org/abs/1705.04932)(BMVC 2017)[[code]](https://github.com/Prinsphield/GeneGAN) :heavy_check_mark: [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [[Paper]](https://arxiv.org/abs/1704.04086)(ICCV 2017) :heavy_check_mark: [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] [[Paper]](https://arxiv.org/abs/1711.09020)[[code]](https://github.com/yunjey/StarGAN)(CVPR 2018) :heavy_check_mark: [Arbitrary Facial Attribute Editing: Only Change What You Want] [[Paper]](https://arxiv.org/abs/1711.10678)[[code]](https://github.com/LynnHo/AttGAN-Tensorflow) :heavy_check_mark: [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes] [[Paper]](https://arxiv.org/abs/1803.10562)[[code]](https://github.com/Prinsphield/ELEGANT)(ECCV 2018) :heavy_check_mark: [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation] [[Paper]](https://arxiv.org/abs/1805.07509)[[code]](https://github.com/zhangqianhui/Sparsely-Grouped-GAN)(ACM MM2018 oral) ## RNN :heavy_check_mark: [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) # Medicine :heavy_check_mark: [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] [[Paper]](https://arxiv.org/abs/1703.05921) ## 3D :heavy_check_mark: [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) :heavy_check_mark: [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [[Web]](http://www.cs.unc.edu/%7Eeunbyung/tvsn/)(CVPR 2017) ## MUSIC :heavy_check_mark: [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [[Paper]](https://arxiv.org/abs/1703.10847)[[HOMEPAGE]](https://richardyang40148.github.io/TheBlog/midinet_arxiv_demo.html) ## For discrete distributions :heavy_check_mark: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1702.07983v1) :heavy_check_mark: [Boundary-Seeking Generative Adversarial Networks] [[Paper]](https://arxiv.org/abs/1702.08431) :heavy_check_mark: [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [[Paper]](https://arxiv.org/abs/1611.04051) ## Improving Classification And Recong :heavy_check_mark: [Generative OpenMax for Multi-Class Open Set Classification] [[Paper]](https://arxiv.org/pdf/1707.07418.pdf)(BMVC 2017) :heavy_check_mark: [Controllable Invariance through Adversarial Feature Learning] [[Paper]](https://arxiv.org/abs/1705.11122)[[code]](https://github.com/github-pengge/adversarial_invariance_feature_learning)(NIPS 2017) :heavy_check_mark: [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] [[Paper]](https://arxiv.org/abs/1701.07717)[[Code]](https://github.com/layumi/Person-reID_GAN) (ICCV2017) :heavy_check_mark: [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, CVPR 2017 Best Paper） # Project :heavy_check_mark: [cleverhans] [[Code]](https://github.com/openai/cleverhans)(A library for benchmarking vulnerability to adversarial examples) :heavy_check_mark: [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) :heavy_check_mark: [HyperGAN] [[Code]](https://github.com/255bits/HyperGAN)(Open source GAN focused on scale and usability) # 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/) | # Tutorial :heavy_check_mark: [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) :heavy_check_mark: [2] [[PDF]](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view)(NIPS Lecun Slides) :heavy_check_mark: [3] [[ICCV 2017 Tutorial About GANS]](https://sites.google.com/view/iccv-2017-gans/schedule)