mlsanityData to 14 Oct 2026

From GAN to WGAN

arXiv 1904.08994 ↗First posted 18 Apr 2019cs.LG stat.MLcited by 34

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Abstract (arXiv)

This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 8; 2022: 11; 2023: 5; 2024: 7; 2025: 2; 2026: 1.

34 papers.

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  1. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  2. 2022Contrastive Laplacian Eigenmapscited by 24
  3. 2022NoisyMix: Boosting Model Robustness to Common Corruptionscited by 17
  4. 2022Astronomia ex machina: a history, primer, and outlook on neural networks in astronomycited by 17
  5. 2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
  6. 2021DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learningcited by 11
  7. 2021WOOD: Wasserstein-based Out-of-Distribution Detectioncited by 9
  8. 2021AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events datacited by 7
  9. 2022Turbo: Opportunistic Enhancement for Edge Video Analyticscited by 7
  10. 2022Generating Synthetic Mobility Networks with Generative Adversarial Networkscited by 5
  11. 2023Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Modelscited by 5
  12. 2021Synthetic flow-based cryptomining attack generation through Generative Adversarial Networkscited by 4
22 more
  1. 2022GAN-based Domain Inference Attackcited by 4
  2. 2024Switched Flow Matching: Eliminating Singularities via Switching ODEscited by 4
  3. 2024Preserving logical and functional dependencies in synthetic tabular datacited by 4
  4. 2021The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHCcited by 3
  5. 2022Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networkscited by 3
  6. 2022Generative Adversarial Networks and Other Generative Modelscited by 3
  7. 2024Structure-aware Fine-tuning for Code Pre-trained Modelscited by 3
  8. 2024Stochastic full waveform inversion with deep generative prior for uncertainty quantificationcited by 2
  9. 2025Diffusion prior as a direct regularization term for FWIcited by 2
  10. 2025Dependency-aware synthetic tabular data generationcited by 2
  11. 2026Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANscited by 2
  12. 2021Regularized Sequential Latent Variable Models with Adversarial Neural Networkscited by 1
  13. 2023Estimating Semantic Similarity between In-Domain and Out-of-Domain Samplescited by 1
  14. 2023Prediction of Diblock Copolymer Morphology via Machine Learningcited by 1
  15. 2024A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspectivecited by 1
  16. 2021FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learningcited by 0
  17. 2021Wasserstein GAN: Deep Generation applied on Bitcoins financial time seriescited by 0
  18. 2022Transfer Learning Based Efficient Traffic Prediction with Limited Training Datacited by 0
  19. 2023SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detectioncited by 0
  20. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  21. 2024Class-specific Data Augmentation for Plant Stress Classificationcited by 0
  22. 2024Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guaranteescited by 0