【新智元导读】生成对抗网络（GAN）的各种变体非常多，atv，GAN 的发明者 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/），atv，或阅读。

　　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 Deions 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

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

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

S?2;GAN— 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

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-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