原文地址：https://blog.csdn.net/chognzhihong_seu/article/details/70941000
GAN? — ?Generative Adversarial Networks
3D-GAN? — ?Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
AC-GAN? —? Conditional Image Synthesis With Auxiliary Classifier GANs
AdaGAN — ?AdaGAN: Boosting Generative Models
AffGAN ?— Amortised MAP Inference for Image Super-resolution
AL-CGAN —? Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
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 ?—? Conditional Generative Adversarial Nets
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
DR-GAN? —? Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
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
GAWWN ?—? Learning What and Where to Draw
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
LAPGAN ?—? Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
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
MAD-GAN ?—? Multi-Agent Diverse 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
SeGAN ?—? SeGAN: Segmenting and Generating the Invisible
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
Softmax-GAN ?—? Softmax GAN
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
Unrolled GAN ?—? Unrolled Generative Adversarial Networks
VGAN ?—? Generating Videos with Scene Dynamics
VGAN ?—? Generative Adversarial Networks as Variational Training of Energy Based Models
VAE-GAN —? Autoencoding beyond pixels using a learned similarity metric
VariGAN ?—? Multi-View Image Generation from a Single-View
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://www.cnblogs.com/lzhu/p/10480878.html