自从Goodfellow2014年提出这个想法之后，生成对抗网络（GAN）就成了深度学习领域内最火的一个概念，包括LeCun在内的许多学者都认为，GAN的出现将会大大推进AI向无监督学习发展的进程。
于是，研究GAN就成了学术圈里的一股风潮，几乎每周，都有关于GAN的全新论文发表。而学者们不仅热衷于研究GAN，还热衷于给自己研究的GAN起名，比如什么3D-GAN、BEGAN、iGAN、S²GAN……千奇百怪、应有尽有。
今天，量子位决定带大家逛逛GANs的动物园（园长：Avinash Hindupur），看看目前世界上到底存活着多少GAN。
GAN — Generative Adversarial Networks
https://arxiv.org/abs/1406.2661
3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
https://arxiv.org/abs/1610.07584
AdaGAN — AdaGAN: Boosting Generative Models
http://arxiv.org/abs/1701.02386v1
AffGAN — Amortised MAP Inference for Image Super-resolution
https://arxiv.org/abs/1610.04490
ALI — Adversarially Learned Inference
https://arxiv.org/abs/1606.00704
AMGAN — Generative Adversarial Nets with Labeled Data by Activation Maximization
http://arxiv.org/abs/1703.02000v1
AnoGAN — Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
http://arxiv.org/abs/1703.05921v1
ArtGAN — ArtGAN: Artwork Synthesis with Conditional Categorial GANs
https://arxiv.org/abs/1702.03410
b-GAN — b-GAN: Unified Framework of Generative Adversarial Networks
https://openreview.net/pdf?id=S1JG13oee
Bayesian GAN — Deep and Hierarchical Implicit Models
https://arxiv.org/abs/1702.08896
BEGAN — BEGAN: Boundary Equilibrium Generative Adversarial Networks
http://arxiv.org/abs/1703.10717v2
BiGAN — Adversarial Feature Learning
http://arxiv.org/abs/1605.09782v7
BS-GAN — Boundary-Seeking Generative Adversarial Networks
http://arxiv.org/abs/1702.08431v1
CGAN — Towards Diverse and Natural Image Descriptions via a Conditional GAN
http://arxiv.org/abs/1703.06029v1
CCGAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
https://arxiv.org/abs/1611.06430v1
CatGAN — Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
http://arxiv.org/abs/1511.06390v2
CoGAN — Coupled Generative Adversarial Networks
http://arxiv.org/abs/1606.07536v2
Context-RNN-GAN — Contextual RNN-GANs for Abstract Reasoning Diagram Generation
https://arxiv.org/abs/1609.09444
C-RNN-GAN — C-RNN-GAN: Continuous recurrent neural networks with adversarial training
https://arxiv.org/abs/1611.09904
CVAE-GAN — CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://arxiv.org/abs/1703.10155
CycleGAN — Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://arxiv.org/abs/1703.10593
DTN — Unsupervised Cross-Domain Image Generation
https://arxiv.org/abs/1611.02200
DCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://arxiv.org/abs/1511.06434
DiscoGAN — Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
http://arxiv.org/abs/1703.05192v1
DualGAN — DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
http://arxiv.org/abs/1704.02510v1
EBGAN — Energy-based Generative Adversarial Network
http://arxiv.org/abs/1609.03126v4
f-GAN — f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
https://arxiv.org/abs/1606.00709
GoGAN — Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
https://arxiv.org/abs/1704.04865
GP-GAN — GP-GAN: Towards Realistic High-Resolution Image Blending
http://arxiv.org/abs/1703.07195v2
IAN — Neural Photo Editing with Introspective Adversarial Networks
https://arxiv.org/abs/1609.07093
iGAN — Generative Visual Manipulation on the Natural Image Manifold
https://arxiv.org/abs/1609.03552v2
IcGAN — Invertible Conditional GANs for image editing
https://arxiv.org/abs/1611.06355
ID-CGAN- Image De-raining Using a Conditional Generative Adversarial Network
http://arxiv.org/abs/1701.05957v3
Improved GAN — Improved Techniques for Training GANs
https://arxiv.org/abs/1606.03498
InfoGAN — InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
http://arxiv.org/abs/1606.03657v1
LR-GAN — LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
http://arxiv.org/abs/1703.01560v1
LSGAN — Least Squares Generative Adversarial Networks
http://arxiv.org/abs/1611.04076v3
LS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
http://arxiv.org/abs/1701.06264v5
MGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
https://arxiv.org/abs/1604.04382
MAGAN — MAGAN: Margin Adaptation for Generative Adversarial Networks
http://arxiv.org/abs/1704.03817v1
MalGAN — Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
http://arxiv.org/abs/1702.05983v1
MARTA-GAN — Deep Unsupervised Representation Learning for Remote Sensing Images
https://arxiv.org/abs/1612.08879
McGAN — McGan: Mean and Covariance Feature Matching GAN
http://arxiv.org/abs/1702.08398v1
MedGAN — Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks
http://arxiv.org/abs/1703.06490v1
MIX+GAN — Generalization and Equilibrium in Generative Adversarial Nets (GANs
https://arxiv.org/abs/1703.00573v3
MPM-GAN — Message Passing Multi-Agent GANs
https://arxiv.org/abs/1612.01294
MV-BiGAN — Multi-view Generative Adversarial Networks
http://arxiv.org/abs/1611.02019v1
pix2pix — Image-to-Image Translation with Conditional Adversarial Networks
https://arxiv.org/abs/1611.07004
PPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
https://arxiv.org/abs/1612.00005
PrGAN — 3D Shape Induction from 2D Views of Multiple Objects
https://arxiv.org/abs/1612.05872
RenderGAN — RenderGAN: Generating Realistic Labeled Data
https://github.com/hindupuravinash/the-gan-zoo/blob/master
RTT-GAN — Recurrent Topic-Transition GAN for Visual Paragraph Generation
http://arxiv.org/abs/1703.07022v2
SGAN — Stacked Generative Adversarial Networks
http://arxiv.org/abs/1612.04357v4
SGAN — Texture Synthesis with Spatial Generative Adversarial Networks
https://arxiv.org/abs/1611.08207
SAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
http://arxiv.org/abs/1611.08788v1
SalGAN — SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
http://arxiv.org/abs/1701.01081v2
SEGAN — SEGAN: Speech Enhancement Generative Adversarial Network
http://arxiv.org/abs/1703.09452v1
SeqGAN — SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
http://arxiv.org/abs/1609.05473v5
SketchGAN — Adversarial Training For Sketch Retrieval
https://arxiv.org/abs/1607.02748
SL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributes
https://arxiv.org/abs/1704.02166
SRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
http://arxiv.org/abs/1609.04802v3
S²GAN — Generative Image Modeling using Style and Structure Adversarial Networks
http://arxiv.org/abs/1603.05631v2
SSL-GAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
https://arxiv.org/abs/1611.06430v1
StackGAN — StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
http://arxiv.org/abs/1612.03242v1
TGAN — Temporal Generative Adversarial Nets
http://arxiv.org/abs/1611.06624v1
TAC-GAN — TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial Network
http://arxiv.org/abs/1703.06412v2
TP-GAN — Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
https://arxiv.org/abs/1704.04086
Triple-GAN — Triple Generative Adversarial Nets
http://arxiv.org/abs/1703.02291v2
VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models
https://arxiv.org/abs/1611.01799
VAE-GAN — Autoencoding beyond pixels using a learned similarity metric
https://arxiv.org/abs/1512.09300
ViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworks
http://arxiv.org/abs/1701.04568v1
WGAN — Wasserstein GAN
http://arxiv.org/abs/1701.07875v2
WGAN-GP — Improved Training of Wasserstein GANs
https://arxiv.org/abs/1704.00028
WaterGAN — WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
http://arxiv.org/abs/1702.07392v1
