Description
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.
Papers Using This Method
Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios2025-06-25Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)2024-11-23Enhanced Anime Image Generation Using USE-CMHSA-GAN2024-11-17Statistical Error Bounds for GANs with Nonlinear Objective Functionals2024-06-24A Differential Equation Approach for Wasserstein GANs and Beyond2024-05-25S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal2024-04-18A CT Image Denoising Method with Residual Encoder-Decoder Network2024-04-02Efficient Generative Modeling via Penalized Optimal Transport Network2024-02-16Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks2024-02-05Adversarial Score Distillation: When score distillation meets GAN2023-12-01Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region2023-09-07ComGAN: Toward GANs Exploiting Multiple Samples2023-04-24Energy-guided Entropic Neural Optimal Transport2023-04-12Diffusion Probabilistic Models beat GANs on Medical Images2022-12-14Resolving Semantic Confusions for Improved Zero-Shot Detection2022-12-12DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics2022-09-26Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN2022-09-21Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?2022-06-15Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks2022-05-24Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning2022-05-09