【论文整理】对抗生成网络必读论文列表！x

　【论文整理】对抗生成网络必读论文列表！

　AdversarialNetsPapers First paper ✔️ [Generative Adversarial Nets] [Paper] [Code](the First paper of GAN) Image Translation ✔️ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [Paper][Code] ✔️ [Image-to-image translation using conditional adversarial nets] [Paper][Code][Code] ✔️ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [Paper][Code] ✔️ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [Paper][Code] ✔️ [CoGAN: Coupled Generative Adversarial Networks] [Paper][Code](NIPS 2016) ✔️ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper](NIPS 2017) ✔️ [Unsupervised Image-to-Image Translation Networks] [Paper] ✔️ [Triangle Generative Adversarial Networks] [Paper] ✔️ [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] [Paper][code] ✔️ [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings] [Paper](Reviewed) ✔️ [UNIT: UNsupervised Image-to-image Translation Networks] [Paper][Code](NIPS 2017) ✔️ [Toward Multimodal Image-to-Image Translation] [Paper][Code](NIPS 2017) ✔️ [Multimodal Unsupervised Image-to-Image Translation] [Paper][Code] ✔️ [Video-to-Video Synthesis] [Paper][Code] ✔️ [Everybody Dance Now] [Paper][Code]

　✔️ [GestureGAN for Hand Gesture-to-Gesture Translation in the Wild] [Paper][Code] ✔️ [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation] [Paper](CVPR 2019) ✔️ [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation] [Paper][Code](CVPR 2019 oral) AutoML ✔️ [AutoGAN: Neural Architecture Search for Generative Adversarial Networks] [Paper][Code](ICCV 2019) Gaze Correction and Redirection ✔️ [Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks] [Paper][Code](ICCV 2019) ✔️ [GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks] [Paper][code] Facial Attribute Manipulation ✔️ [Autoencoding beyond pixels using a learned similarity metric] [Paper][code][Tensorflow code] ✔️ [Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code]（NIPS）

　✔️ [Invertible Conditional GANs for image editing] [Paper][Code] ✔️ [Learning Residual Images for Face Attribute Manipulation] [Paper][code](CVPR 2017) ✔️ [Neural Photo Editing with Introspective Adversarial Networks] [Paper][Code](ICLR 2017) ✔️ [Neural Face Editing with Intrinsic Image Disentangling] [Paper](CVPR 2017) ✔️ [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] [Paper][code](BMVC 2017) ✔️ [ST-GAN: Unsupervised Facial Image Semantic Transformation Using Generative Adversarial Networks] [Paper] ✔️ [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [Paper](ICCV 2017) ✔️ [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] [Paper][code](CVPR 2018)

　✔️ [Arbitrary Facial Attribute Editing: Only Change What You Want] [Paper][code] ✔️ [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes] [Paper][code](ECCV 2018) ✔️ [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation] [Paper][code](ACM MM2018 oral) ✔️ [GANimation: Anatomically-aware Facial Animation from a Single Image] [Paper][code](ECCV 2018 oral) ✔️ [Geometry Guided Adversarial Facial Expression Synthesis] [Paper](ACMMM 2018) ✔️ [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing] [Paper][code](CVPR 2019) Generation High-Quality Images ✔️ [Unsupervised Representation Learning ith Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)(ICLR) ✔️ [Generative Adversarial Text to Image Synthesis] [Paper][Code][code] ✔️ [Improved Techniques for Training GANs] [Paper][Code](Goodfellow’s paper) ✔️ [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code] ✔️ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code] ✔️ [Improved Training of Wasserstein GANs] [Paper][Code] ✔️ [Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow] [Paper][Code] ✔️ [Progressive Growing of GANs for Improved Quality, Stability, and Variation] [Paper][Code][Tensorflow Code] ✔️ [ Self-Attention Generative Adversarial Networks ] [Paper][Code](NIPS 2018) ✔️ [Large Scale GAN Training for High Fidelity Natural Image Synthesis] [Paper](ICLR 2019) ✔️ [A Style-Based Generator Architecture for Generative Adversarial Networks] [Paper][Code] ✔️ [SinGAN: Learning a Generative Model from a Single Natural Image] [Paper][Code](ICCV2019 best paper)

　Unclassified ✔️ [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code] ✔️ [Adversarial Autoencoders] [Paper][Code] ✔️ [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [Paper] ✔️ [Generating images with recurrent adversarial networks] [Paper][Code] ✔️ [Generative Visual Manipulation on the Natural Image Manifold] [Paper][Code] ✔️ [Learning What and Where to Draw] [Paper][Code] ✔️ [Adversarial Training for Sketch Retrieval] [Paper] ✔️ [Generative Image Modeling using Style and Structure Adversarial Networks] [Paper][Code] ✔️ [Generative Adversarial Networks as Variational Training of Energy Based Models] [Paper](ICLR 2017) ✔️ [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [Paper][Code] ✔️ [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [Paper][Code] ✔️ [Adversarial Feature Learning] [Paper] ✔️ [Adversarially Learned Inference][Paper][Code] GAN Theory ✔️ [Energy-based generative adversarial network] [Paper][Code](Lecun paper) ✔️ [Improved Techniques for Training GANs] [Paper][Code](Goodfellow’s paper) ✔️ [Mode Regularized Generative Adversarial Networks] [Paper](Yoshua Bengio , ICLR 2017) ✔️ [Improving Generative Adversarial Networks with Denoising Feature Matching] [Paper][Code](Yoshua Bengio , ICLR 2017) ✔️ [Sampling Generative Networks] [Paper][Code] ✔️ [How to train Gans] [Docu] ✔️ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper](ICLR 2017)

　✔️ [Unrolled Generative Adversarial Networks] [Paper][Code](ICLR 2017) ✔️ [Least Squares Generative Adversarial Networks] [Paper][Code](ICCV 2017) ✔️ [Wasserstein GAN] [Paper][Code] ✔️ [Improved Training of Wasserstein GANs] [Paper][Code](The improve of wgan) ✔️ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper] ✔️ [Generalization and Equilibrium in Generative Adversarial Nets] [Paper]（ICML 2017）

　✔️ [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium][Paper][code] ✔️ [Spectral Normalization for Generative Adversarial Networks][Paper][code]（ICLR 2018）

　✔️ [Which Training Methods for GANs do actually Converge][Paper][code]（ICML 2018）

　✔️ [Self-Supervised Generative Adversarial Networks][Paper][code]（CVPR 2019）

　Scene Generation ✔️ [a layer-based sequential framework for scene generation with gans] [Paper][Code](AAAI 2019) Semi-Supervised Learning ✔️ [Adversarial Training Methods for Semi-Supervised Text Classification] [Paper][Note]( Ian Goodfellow Paper) ✔️ [Improved Techniques for Training GANs] [Paper][Code](Goodfellow’s paper) ✔️ [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper](ICLR) ✔️ [Semi-Supervised QA with Generative Domain-Adaptive Nets] [Paper](ACL 2017) ✔️ [Good Semi-supervised Learning that Requires a Bad GAN] [Paper][Code](NIPS 2017) Ensemble ✔️ [AdaGAN: Boosting Generative Models] [Paper][[Code]]（Google Brain）

　Image blending ✔️ [GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper][Code] Image Inpainting ✔️ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code](CVPR 2017) ✔️ [Context Encoders: Feature Learning by Inpainting] [Paper][Code] ✔️ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper] ✔️ [Generative face completion] [Paper][code](CVPR2017) ✔️ [Globally and Locally Consistent Image Completion] [MainPAGE][code](SIGGRAPH 2017) ✔️ [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis] [Paper][code](CVPR 2017) ✔️ [Eye In-Painting with Exemplar Generative Adversarial Networks] [Paper][Introduction][Tensorflow code](CVPR2018) ✔️ [Generative Image Inpainting with Contextual Attention] [Paper][Project][Demo][YouTube][Code](CVPR2018) ✔️ [Free-Form Image Inpainting with Gated Convolution] [Paper][Project][YouTube] ✔️ [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning] [Paper][Code] Re-identification ✔️ [Joint Discriminative and Generative Learning for Person Re-identification] [Paper][Code][YouTube] [Bilibili] (CVPR2019 Oral) ✔️ [Pose-Normalized Image Generation for Person Re-identification] [Paper][Code](ECCV 2018) Super-Resolution ✔️ [Image super-resolution through deep learning ][Code](Just for face dataset) ✔️ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [Paper][Code]（Using Deep residual network）

　✔️ [EnhanceGAN] [Docs][[Code]]

　✔️ [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks] [Paper][Code](ECCV 2018 workshop) De-Occlusion ✔️ [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper] Semantic Segmentation ✔️ [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [Paper][Code] ✔️ [Semantic Segmentation using Adversarial Networks] [Paper]（soumith’s paper）

　Object Detection ✔️ [Pceptual generative adversarial networks for small object detection] [Paper](CVPR 2017) ✔️ [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [Paper][code](CVPR2017) Landmark Detection ✔️ [Style aggregated network for facial landmark detection] [Paper](CVPR 2018) Conditional Adversarial ✔️ [Conditional Generative Adversarial Nets] [Paper][Code] ✔️ [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [Paper][Code][Code] ✔️ [Conditional Image Synthesis With Auxiliary Classifier GANs] [Paper][Code](GoogleBrain ICLR 2017) ✔️ [Pixel-Level Domain Transfer] [Paper][Code] ✔️ [Invertible Conditional GANs for image editing] [Paper][Code] ✔️ [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code] ✔️ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code] Video Prediction and Generation ✔️ [Deep multi-scale video prediction beyond mean square error] [Paper][Code](Yann LeCun’s paper)

　✔️ [Generating Videos with Scene Dynamics] [Paper][Web][Code] ✔️ [MoCoGAN: Decomposing Motion and Content for Video Generation] [Paper] Texture Synthesis & style transfer ✔️ [Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper][Code](ECCV 2016) ✔️ [Controllable Artistic Text Style Transfer via Shape-Matching GAN] [Paper][Code](ICCV 2019) Shadow Detection and Removal ✔️ [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal] [Paper][Code](ICCV 2019) Makeup ✔️ [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network] [Paper](ACMMM 2018) Reinforcement learning ✔️ [Connecting Generative Adversarial Networks and Actor-Critic Methods] [Paper](NIPS 2016 workshop) RNN ✔️ [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [Paper][Code] ✔️ [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient] [Paper][Code](AAAI 2017) Medicine ✔️ [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] [Paper] 3D ✔️ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [Paper][Web][code](2016 NIPS) ✔️ [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [Web](CVPR 2017)

　MUSIC ✔️ [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [Paper][HOMEPAGE] For discrete distributions ✔️ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [Paper] ✔️ [Boundary-Seeking Generative Adversarial Networks] [Paper] ✔️ [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [Paper] Improving Classification And Recong ✔️ [Generative OpenMax for Multi-Class Open Set Classification] [Paper](BMVC 2017) ✔️ [Controllable Invariance through Adversarial Feature Learning] [Paper][code](NIPS 2017) ✔️ [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] [Paper][Code] (ICCV2017) ✔️ [Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code]（Apple paper, CVPR 2017 Best Paper）

　✔️ [GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification] [Paper] (Neurocomputing Journal (2018), Elsevier）

　Project ✔️ [cleverhans] [Code](A library for benchmarking vulnerability to adversarial examples) ✔️ [reset-cppn-gan-tensorflow] [Code](Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images) ✔️ [HyperGAN] [Code](Open source GAN focused on scale and usability) Blogs Author Address inFERENCe Adversarial network

　inFERENCe InfoGan distill Deconvolution and Image Generation yingzhenli Gan theory OpenAI Generative model Tutorial ✔️ [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details] ✔️ [2] [PDF](NIPS Lecun Slides) ✔️ [3] [ICCV 2017 Tutorial About GANS]