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、S⊃2;GAN……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 Deions 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

S⊃2;GAN— 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

GAN Zoo:千奇百怪的生成对抗网络,都在这里了