Wasserstein GAN

Introduced by Arjovsky et al. in Wasserstein GAN

Wasserstein GAN, or WGAN, is a type of generative adversarial network that minimizes an approximation of the Earth-Mover's distance (EM) rather than the Jensen-Shannon divergence as in the original GAN formulation. It leads to more stable training than original GANs with less evidence of mode collapse, as well as meaningful curves that can be used for debugging and searching hyperparameters.

Source: Wasserstein GAN

Latest Papers

PAPER DATE
Adaptive WGAN with loss change rate balancing
Xu OuyangGady Agam
2020-08-28
Direct Adversarial Training for GANs
Ziqiang Li
2020-08-19
Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data
Tianci LiuJeffrey Regier
2020-06-17
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models
Yun LuoLi-Zhen ZhuZi-Yu WanBao-Liang Lu
2020-06-04
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Yuxuan SongQiwei YeMinkai XuTie-Yan Liu
2020-04-05
Audio inpainting with generative adversarial network
| P. P. EbnerA. Eltelt
2020-03-13
Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks
Antoine Delplace
2020-01-10
iWGAN: an Autoencoder WGAN for Inference
Anonymous
2020-01-01
A GOODNESS OF FIT MEASURE FOR GENERATIVE NETWORKS
Anonymous
2020-01-01
Study of Constrained Network Structures for WGANs on Numeric Data Generation
Wei WangChuang WangTao CuiYue Li
2019-11-05
Quantum Wasserstein Generative Adversarial Networks
| Shouvanik ChakrabartiYiming HuangTongyang LiSoheil FeiziXiaodi Wu
2019-10-31
Bridging the Gap Between $f$-GANs and Wasserstein GANs
Jiaming SongStefano Ermon
2019-10-22
Wasserstein GAN With Quadratic Transport Cost
Huidong Liu Xianfeng Gu Dimitris Samaras
2019-10-01
Model Imitation for Model-Based Reinforcement Learning
Yueh-Hua WuTing-Han FanPeter J. RamadgeHao Su
2019-09-25
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
Sergio GarridoStanislav S. BorysovFrancisco C. PereiraJeppe Rich
2019-09-17
A Characteristic Function Approach to Deep Implicit Generative Modeling
| Abdul Fatir AnsariJonathan ScarlettHarold Soh
2019-09-16
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
Yicheng ChenAngela JakarySivakami AvadiappanChristopher P. HessJanine M. Lupo
2019-05-08
Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
Cheolhyeong KimSeungtae ParkHyung Ju Hwang
2019-05-01
Generative model based on minimizing exact empirical Wasserstein distance
Akihiro IoharaTakahito OgawaToshiyuki Tanaka
2019-05-01
Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space
Boli FangChuck JiaMiao JiangDhawal Chaturvedi
2019-05-01
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
| Juan Zamudio-FernandezAtakan OkanFrancisco Villaescusa-NavarroSeda BilalogluAsena Derin CengizSiyu HeLaurence Perreault LevasseurShirley Ho
2019-04-29
UU-Nets Connecting Discriminator and Generator for Image to Image Translation
Wu Jionghao
2019-04-04
Lipschitz Generative Adversarial Nets
| Zhiming ZhouJiadong LiangYuxuan SongLantao YuHongwei WangWeinan ZhangYong YuZhihua Zhang
2019-02-15
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton MallastoJes FrellsenWouter BoomsmaAasa Feragen
2019-02-10
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
| Jin ZhuGuang YangPietro Lio
2019-01-10
On Relativistic $f$-Divergences
| Alexia Jolicoeur-Martineau
2019-01-08
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
| Rahul DeyFelix Juefei-XuVishnu Naresh BoddetiMarios Savvides
2018-12-19
A Wasserstein GAN model with the total variational regularization
Lijun ZhangYujin ZhangYongbin Gao
2018-12-03
Metropolis-Hastings Generative Adversarial Networks
| Ryan TurnerJane HungEric FrankYunus SaatciJason Yosinski
2018-11-28
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Rahul SinghViraj ShahBalaji PokuriSoumik SarkarBaskar GanapathysubramanianChinmay Hegde
2018-11-21
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
| Jianlin Su
2018-11-18
Deep learning framework DNN with conditional WGAN for protein solubility prediction
X. HanL. ZhangK. ZhouX. Wang
2018-11-17
Deep Bayesian Inversion
| Jonas AdlerOzan Öktem
2018-11-14
Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN
Cheolhyeong KimSeungtae ParkHyung Ju Hwang
2018-10-05
GANs beyond divergence minimization
| Alexia Jolicoeur-Martineau
2018-09-06
A Two-Step Computation of the Exact GAN Wasserstein Distance
Huidong LiuXianfeng GUDimitris Samaras
2018-07-01
Training Discriminative Models to Evaluate Generative Ones
Timothée LesortAndrei StoainJean-François GoudouDavid Filliat
2018-06-28
Banach Wasserstein GAN
| Jonas AdlerSebastian Lunz
2018-06-18
Language Modeling with Generative AdversarialNetworks
Mehrad MoradshahiUtkarsh Contractor
2018-04-08
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Xiang WeiBoqing GongZixia LiuWei LuLiqiang Wang
2018-03-05
Robust GANs against Dishonest Adversaries
Zhi XuChengtao LiStefanie Jegelka
2018-02-27
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
Atanas MirchevSeyed-Ahmad Ahmadi
2018-01-26
Deep Lipschitz networks and Dudley GANs
Ehsan AbbasnejadJaven ShiAnton van den Hengel
2018-01-01
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Zhiwu HuangJiqing WuLuc Van Gool
2017-12-05
Training GANs with Optimism
| Constantinos DaskalakisAndrew IlyasVasilis SyrgkanisHaoyang Zeng
2017-10-31
A Geometric View of Optimal Transportation and Generative Model
Na LeiKehua SuLi CuiShing-Tung YauDavid Xianfeng Gu
2017-10-16
Statistics of Deep Generated Images
| Yu ZengHuchuan LuAli Borji
2017-08-09
Wasserstein Generative Adversarial Networks
Martin ArjovskySoumith ChintalaLéon Bottou
2017-08-01
Linear Discriminant Generative Adversarial Networks
Zhun SunMete OzayTakayuki Okatani
2017-07-25
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
| Felix Juefei-XuVishnu Naresh BoddetiMarios Savvides
2017-04-17
Wasserstein GAN
| Martin ArjovskySoumith ChintalaLéon Bottou
2017-01-26

Tasks

TASK PAPERS SHARE
Image Generation 9 40.91%
Super-Resolution 2 9.09%
Density Estimation 1 4.55%
EEG 1 4.55%
Emotion Recognition 1 4.55%
Image-to-Image Translation 1 4.55%
Image Super-Resolution 1 4.55%
Face Generation 1 4.55%
Decision Making 1 4.55%

Components

COMPONENT TYPE
Convolution
Convolutions

Categories