GAN动物园——GAN的各种变体列表

摘要：生成对抗网络的各种变体非常多，的发明者在上推荐了这份名为的各种变体列表，这也表明现在确实非常火，被应用于各种各样的任务。了解这些各种各样的，或许能对你创造自己的有所启发。这篇文章列举了目前出现的各种变体，并将长期更新。

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。

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，也可在深度学习世界公众号回复【变体论文】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

商业智能与数据分析群

兴趣范围包括各种让数据产生价值的办法，实际应用案例分享与讨论，分析工具，ETL工具，数据仓库，数据挖掘工具，报表系统等全方位知识

QQ群：81035754