GAN 論文大彙總

做者：chen_h
微信號 & QQ：862251340
微信公衆號：coderpai
簡書地址：https://www.jianshu.com/p/b7f...

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關於生成對抗網絡（GAN）的新論文每週都會出現不少，跟蹤發現他們很是難，更不用說去辨別那些研究人員對 GAN 各類奇奇怪怪，使人難以置信的創造性的命名！固然，你能夠經過閱讀 OpanAI 的博客或者 KDNuggets 中的概述性閱讀教程，瞭解更多的有關 GAN 的信息。

在這裏彙總了一個如今和常用的GAN論文，全部文章都連接到了 Arxiv 上面。

• GAN — Generative Adversarial Networks
• 3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• AC-GAN — Conditional Image Synthesis With Auxiliary Classifier GANs
• AdaGAN — AdaGAN: Boosting Generative Models
• AffGAN — Amortised MAP Inference for Image Super-resolution
• AL-CGAN — Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
• ALI — Adversarially Learned Inference
• AMGAN — Generative Adversarial Nets with Labeled Data by Activation Maximization
• AnoGAN — Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
• ArtGAN — ArtGAN: Artwork Synthesis with Conditional Categorial GANs
• b-GAN — b-GAN: Unified Framework of Generative Adversarial Networks
• Bayesian GAN — Deep and Hierarchical Implicit Models
• BEGAN — BEGAN: Boundary Equilibrium Generative Adversarial Networks
• BiGAN — Adversarial Feature Learning
• BS-GAN — Boundary-Seeking Generative Adversarial Networks
• CGAN — Conditional Generative Adversarial Nets
• CCGAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
• CatGAN — Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• CoGAN — Coupled Generative Adversarial Networks
• Context-RNN-GAN — Contextual RNN-GANs for Abstract Reasoning Diagram Generation
• C-RNN-GAN — C-RNN-GAN: Continuous recurrent neural networks with adversarial training
• CVAE-GAN — CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
• CycleGAN — Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• DTN — Unsupervised Cross-Domain Image Generation
• DCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• DiscoGAN — Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
• DR-GAN — Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
• DualGAN — DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
• EBGAN — Energy-based Generative Adversarial Network
• f-GAN — f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• GAWWN — Learning What and Where to Draw
• GoGAN — Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
• GP-GAN — GP-GAN: Towards Realistic High-Resolution Image Blending
• IAN — Neural Photo Editing with Introspective Adversarial Networks
• iGAN — Generative Visual Manipulation on the Natural Image Manifold
• IcGAN — Invertible Conditional GANs for image editing
• ID-CGAN- Image De-raining Using a Conditional Generative Adversarial Network
• Improved GAN — Improved Techniques for Training GANs
• InfoGAN — InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• LAPGAN — Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• LR-GAN — LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
• LSGAN — Least Squares Generative Adversarial Networks
• LS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
• MGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
• MAGAN — MAGAN: Margin Adaptation for Generative Adversarial Networks
• MAD-GAN — Multi-Agent Diverse Generative Adversarial Networks
• MalGAN — Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• MARTA-GAN — Deep Unsupervised Representation Learning for Remote Sensing Images
• McGAN — McGan: Mean and Covariance Feature Matching GAN
• MedGAN — Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks
• MIX+GAN — Generalization and Equilibrium in Generative Adversarial Nets (GANs)
• MPM-GAN — Message Passing Multi-Agent GANs
• MV-BiGAN — Multi-view Generative Adversarial Networks
• pix2pix — Image-to-Image Translation with Conditional Adversarial Networks
• PPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
• PrGAN — 3D Shape Induction from 2D Views of Multiple Objects
• RenderGAN — RenderGAN: Generating Realistic Labeled Data
• RTT-GAN — Recurrent Topic-Transition GAN for Visual Paragraph Generation
• SGAN — Stacked Generative Adversarial Networks
• SGAN — Texture Synthesis with Spatial Generative Adversarial Networks
• SAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
• SalGAN — SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
• SEGAN — SEGAN: Speech Enhancement Generative Adversarial Network
• SeGAN — SeGAN: Segmenting and Generating the Invisible
• SeqGAN — SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
• SketchGAN — Adversarial Training For Sketch Retrieval
• SL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributes
• Softmax-GAN — Softmax GAN
• SRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
• S²GAN — Generative Image Modeling using Style and Structure Adversarial Networks
• SSL-GAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
• StackGAN — StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
• TGAN — Temporal Generative Adversarial Nets
• TAC-GAN — TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial Network
• TP-GAN — Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
• Triple-GAN — Triple Generative Adversarial Nets
• Unrolled GAN — Unrolled Generative Adversarial Networks
• VGAN — Generating Videos with Scene Dynamics
• VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models
• VAE-GAN — Autoencoding beyond pixels using a learned similarity metric
• VariGAN — Multi-View Image Generation from a Single-View
• ViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworks
• WGAN — Wasserstein GAN
• WGAN-GP — Improved Training of Wasserstein GANs
• WaterGAN — WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

若是你對 GAN 感興趣，能夠訪問這個專題。歡迎交流。

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做者：chen_h
微信號 & QQ：862251340
簡書地址：https://www.jianshu.com/p/b7f...

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