Wasserstein Generative Adversarial Network

Wasserstein Generative Adversarial Networks (WGANs) are generative models that leverage the Wasserstein distance to improve training stability and sample quality compared to traditional GANs. Current research focuses on extending WGANs for conditional generation, addressing data scarcity through augmentation, and improving their application in diverse fields like image generation, time series analysis, and inverse problems. This work is significant because WGANs offer a robust framework for generating high-quality synthetic data, enabling advancements in areas with limited real-world data and facilitating improved model training and performance in various applications.

Papers

September 27, 2024

Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks
Yixuan Qiu, Qingyi Gao, Xiao Wang
Generative Model Generative Adversarial Network Latent Space Adaptive Learning Wasserstein Generative Adversarial Network Wasserstein Gan Latent Dimension

September 16, 2024

Partial Distribution Matching via Partial Wasserstein Adversarial Networks
Zi-Ming Wang, Nan Xue, Ling Lei, Rebecka Jörnsten, Gui-Song Xia
Matching Accuracy Distribution Matching Wasserstein Generative Adversarial Network Partial Domain Adaptation

March 27, 2024

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald
Financial Application Wasserstein Distance Flow Matching Bayesian Inverse Problem Wasserstein Generative Adversarial Network

March 17, 2024

Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
Silvia Corbara, Alejandro Moreo
Adversarial Attack Data Augmentation Manual Effort Wasserstein Generative Adversarial Network Authorship Verification Adversarial Setting Diverse Forgery Generation Image

March 1, 2024

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
Kawana Stalin, Mikias Berhanu Mekoya
Generative Adversarial Network Data Augmentation GAN Model Android Malware Wasserstein Generative Adversarial Network

February 26, 2024

Penalized Generative Variable Selection
Tong Wang, Jian Huang, Shuangge Ma
High Dimensional Variable Selection Wasserstein Generative Adversarial Network

February 5, 2024

Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
Joshua Park, Priyanshu Mahey, Ore Adeniyi
EEG Signal EEG Classification Wasserstein Generative Adversarial Network Wasserstein Gan

December 1, 2023

Adversarial Score Distillation: When score distillation meets GAN
Min Wei, Jingkai Zhou, Junyao Sun, Xuesong Zhang
GAN Model Score Distillation Score Distillation Sampling Wasserstein Generative Adversarial Network

October 20, 2023

Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances
Jannis Chemseddine, Paul Hagemann, Christian Wald
Wasserstein Distance Conditional Generative Posterior Approximation Wasserstein Generative Adversarial Network Coupling Effect

October 12, 2023

August 9, 2023

Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects
Kamil Kwarciak, Marek Wodzinski
Variational Autoencoder Deep Generative Wasserstein Generative Adversarial Network Cranial Defect Cranial Implant

July 14, 2023

Generative adversarial networks for data-scarce spectral applications
Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad
Generative Adversarial Network Adversarial Learning Generative Question Wasserstein Generative Adversarial Network Wasserstein Gan Synthetic Spectrum Spectral Band

June 15, 2023

Improving Path Planning Performance through Multimodal Generative Models with Local Critics
Jorge Ocampo Jimenez, Wael Suleiman
Motion Planning Variational Auto Wasserstein Generative Adversarial Network Multimodal Generative Model Configuration Space

June 3, 2023

A Conditional Generative Chatbot using Transformer Model
Nura Esfandiari, Kourosh Kiani, Razieh Rastgoo
Generative AI Chatbot Response Answer Generation Wasserstein Generative Adversarial Network

March 22, 2023

Wasserstein Adversarial Examples on Univariant Time Series Data
Wenjie Wang, Li Xiong, Jian Lou
Adversarial Attack Adversarial Example Time Series Wasserstein Distance Wasserstein Space Wasserstein Generative Adversarial Network

March 11, 2023

Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200
S. Li, I. Ostrovskiy, Z. Li, L. Yang, S. Al Kharusi, G. Anton, I. Badhrees, P. S. Barbeau, D. Beck, V. Belov, T. Bhatta, M. Breidenbach, T. Brunner, G. F. Cao, W. R. Cen, C. Chambers, B. Cleveland, M. Coon, A. Craycraft, T. Daniels, L. Darroch, S. J. Daugherty, J. Davis, S. Delaquis, A. Der Mesrobian-Kabakian, R. DeVoe, J. Dilling, A. Dolgolenko, M. J. Dolinski, J. Echevers, W. Fairbank, D. Fairbank, J. Farine, S. Feyzbakhsh, P. Fierlinger, Y. S. Fu, D. Fudenberg, P. Gautam, R. Gornea, G. Gratta, C. Hall, E. V. Hansen, J. Hoessl, P. Hufschmidt, M. Hughes, A. Iverson, A. Jamil, C. Jessiman, M. J. Jewell, A. Johnson, A. Karelin, L. J. Kaufman, T. Koffas, R. Krücken, A. Kuchenkov, K. S. Kumar, Y. Lan, A. Larson, B. G. Lenardo, D. S. Leonard, G. S. Li, C. Licciardi, Y. H. Lin, R. MacLellan, T. McElroy, T. Michel, B. Mong, D. C. Moore, K. Murray, O. Njoya, O. Nusair, A. Odian, A. Perna, A. Piepke, A. Pocar, F. Retière, A. L. Robinson, P. C. Rowson, J. Runge, S. Schmidt, D. Sinclair, K. Skarpaas, A. K. Soma, V. Stekhanov, M. Tarka, S. Thibado, J. Todd, T. Tolba, T. I. Totev, R. Tsang
Generative Adversarial Network Wasserstein Generative Adversarial Network Amplitude Scintillation

January 15, 2023

Inpainting borehole images using Generative Adversarial Networks
Rachid Belmeskine, Abed Benaichouche
Generative Adversarial Network GAN Based Wasserstein Generative Adversarial Network Borehole Description

December 10, 2022

Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Yee-Fan Tan, Chee-Ming Ting, Fuad Noman, Raphaël C. -W. Phan, Hernando Ombao
Functional Connectivity Resting State Wasserstein Generative Adversarial Network

October 24, 2022

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
Matthew Bendel, Rizwan Ahmad, Philip Schniter
Regularization Model Posterior Sampling Inception Distance Wasserstein Generative Adversarial Network Conditional GAN Image Recovery