生成对抗网络论文

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