Ian Goodfellow推荐:GAN动物园——GAN的各种变体列表(下载)

2017 年 4 月 21 日 新智元

  新智元编译  

来源:deephunt.in

作者: Avinash Hindupur


【新智元导读】生成对抗网络(GAN)的各种变体非常多,GAN 的发明者 Ian Goodfellow 在Twitter上推荐了这份名为“The GAN Zoo”的各种GAN变体列表,这也表明现在GAN确实非常火,被应用于各种各样的任务。了解这些各种各样的GAN,或许能对你创造自己的 X-GAN有所启发。


在新智元公众号回复【170421】下载以下全部论文



几乎每周都有新的关于生成对抗网络(GAN)的论文出现,而且你很难跟踪到它们,因为研究者为这些 GAN 命名的方式非常具有创造性。了解有关 GAN 的更多信息,可以参考 OpenAI 博客的一份非常全面的 GAN 综述文章(地址:https://blog.openai.com/generative-models/),或阅读王飞跃等人的 GAN 综述文章


这篇文章列举了目前出现的各种GAN变体,并将长期更新。这是一个开源的项目,你也可以通过 pull request 添加作者没有注意到的 GAN,


GitHub 地址:https://github.com/hindupuravinash/the-gan-zoo


这份列表的形式是:名称——论文标题(论文均发表在Arxiv,也可在新智元公众号回复【170421】下载)。


  • GAN  — Generative Adversarial Networks


  • 3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling


  • AdaGAN — AdaGAN: Boosting Generative Models


  • AffGAN — Amortised MAP Inference for Image Super-resolution


  • 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 — Towards Diverse and Natural Image Descriptions via a Conditional GAN


  • 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


  • 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


  • 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


  • 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


  • 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


  • 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


  • 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


  • VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models


  • VAE-GAN — Autoencoding beyond pixels using a learned similarity metric


  • 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


原文地址:https://deephunt.in/the-gan-zoo-79597dc8c347



3月27日,新智元开源·生态AI技术峰会暨新智元2017创业大赛颁奖盛典隆重召开,包括“BAT”在内的中国主流 AI 公司、600多名行业精英齐聚,共同为2017中国人工智能的发展画上了浓墨重彩的一笔。


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