作者：chen_h 微信号 & QQ：862251340 微信公众号：coderpai 简书地址：https://www.jianshu.com/p/b7f6c88027f0
关于生成对抗网络（GAN）的新论文每周都会出现很多，跟踪发现他们非常难，更不用说去辨别那些研究人员对 GAN 各种奇奇怪怪，令人难以置信的创造性的命名！当然，你可以通过阅读 OpanAI 的博客或者 KDNuggets 中的概述性阅读教程，了解更多的有关 GAN 的信息。
在这里汇总了一个现在和经常使用的GAN论文，所有文章都链接到了 Arxiv 上面。
GAN — Generative Adversarial Networks3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial ModelingAC-GAN — Conditional Image Synthesis With Auxiliary Classifier GANsAdaGAN — AdaGAN: Boosting Generative ModelsAffGAN — Amortised MAP Inference for Image Super-resolutionAL-CGAN — Learning to Generate Images of Outdoor Scenes from Attributes and Semantic LayoutsALI — Adversarially Learned InferenceAMGAN — Generative Adversarial Nets with Labeled Data by Activation MaximizationAnoGAN — Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker DiscoveryArtGAN — ArtGAN: Artwork Synthesis with Conditional Categorial GANsb-GAN — b-GAN: Unified Framework of Generative Adversarial NetworksBayesian GAN — Deep and Hierarchical Implicit ModelsBEGAN — BEGAN: Boundary Equilibrium Generative Adversarial NetworksBiGAN — Adversarial Feature LearningBS-GAN — Boundary-Seeking Generative Adversarial NetworksCGAN — Conditional Generative Adversarial NetsCCGAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial NetworksCatGAN — Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCoGAN — Coupled Generative Adversarial NetworksContext-RNN-GAN — Contextual RNN-GANs for Abstract Reasoning Diagram GenerationC-RNN-GAN — C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingCVAE-GAN — CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCycleGAN — Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksDTN — Unsupervised Cross-Domain Image GenerationDCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial NetworksDiscoGAN — Learning to Discover Cross-Domain Relations with Generative Adversarial NetworksDR-GAN — Disentangled Representation Learning GAN for Pose-Invariant Face RecognitionDualGAN — DualGAN: Unsupervised Dual Learning for Image-to-Image TranslationEBGAN — Energy-based Generative Adversarial Networkf-GAN — f-GAN: Training Generative Neural Samplers using Variational Divergence MinimizationGAWWN — Learning What and Where to DrawGoGAN — Gang of GANs: Generative Adversarial Networks with Maximum Margin RankingGP-GAN — GP-GAN: Towards Realistic High-Resolution Image BlendingIAN — Neural Photo Editing with Introspective Adversarial NetworksiGAN — Generative Visual Manipulation on the Natural Image ManifoldIcGAN — Invertible Conditional GANs for image editingID-CGAN- Image De-raining Using a Conditional Generative Adversarial NetworkImproved GAN — Improved Techniques for Training GANsInfoGAN — InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsLAPGAN — Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksLR-GAN — LR-GAN: Layered Recursive Generative Adversarial Networks for Image GenerationLSGAN — Least Squares Generative Adversarial NetworksLS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz DensitiesMGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial NetworksMAGAN — MAGAN: Margin Adaptation for Generative Adversarial NetworksMAD-GAN — Multi-Agent Diverse Generative Adversarial NetworksMalGAN — Generating Adversarial Malware Examples for Black-Box Attacks Based on GANMARTA-GAN — Deep Unsupervised Representation Learning for Remote Sensing ImagesMcGAN — McGan: Mean and Covariance Feature Matching GANMedGAN — Generating Multi-label Discrete Electronic Health Records using Generative Adversarial NetworksMIX+GAN — Generalization and Equilibrium in Generative Adversarial Nets (GANs)MPM-GAN — Message Passing Multi-Agent GANsMV-BiGAN — Multi-view Generative Adversarial Networkspix2pix — Image-to-Image Translation with Conditional Adversarial NetworksPPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent SpacePrGAN — 3D Shape Induction from 2D Views of Multiple ObjectsRenderGAN — RenderGAN: Generating Realistic Labeled DataRTT-GAN — Recurrent Topic-Transition GAN for Visual Paragraph GenerationSGAN — Stacked Generative Adversarial NetworksSGAN — Texture Synthesis with Spatial Generative Adversarial NetworksSAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial NetworksSalGAN — SalGAN: Visual Saliency Prediction with Generative Adversarial NetworksSEGAN — SEGAN: Speech Enhancement Generative Adversarial NetworkSeGAN — SeGAN: Segmenting and Generating the InvisibleSeqGAN — SeqGAN: Sequence Generative Adversarial Nets with Policy GradientSketchGAN — Adversarial Training For Sketch RetrievalSL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributesSoftmax-GAN — Softmax GANSRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial NetworkS²GAN — Generative Image Modeling using Style and Structure Adversarial NetworksSSL-GAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial NetworksStackGAN — StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial NetworksTGAN — Temporal Generative Adversarial NetsTAC-GAN — TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial NetworkTP-GAN — Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View SynthesisTriple-GAN — Triple Generative Adversarial NetsUnrolled GAN — Unrolled Generative Adversarial NetworksVGAN — Generating Videos with Scene DynamicsVGAN — Generative Adversarial Networks as Variational Training of Energy Based ModelsVAE-GAN — Autoencoding beyond pixels using a learned similarity metricVariGAN — Multi-View Image Generation from a Single-ViewViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworksWGAN — Wasserstein GANWGAN-GP — Improved Training of Wasserstein GANsWaterGAN — WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images如果你对 GAN 感兴趣，可以访问这个专题。欢迎交流。
作者：chen_h 微信号 & QQ：862251340 简书地址：https://www.jianshu.com/p/b7f6c88027f0
CoderPai 是一个专注于算法实战的平台，从基础的算法到人工智能算法都有设计。如果你对算法实战感兴趣，请快快关注我们吧。加入AI实战微信群，AI实战QQ群，ACM算法微信群，ACM算法QQ群。长按或者扫描如下二维码，关注 “CoderPai” 微信号（coderpai）
