# From GAN to WGAN

arXiv: https://arxiv.org/abs/1904.08994 · HTML: https://mlsanity.com/paper/1904.08994/

## Key facts

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

## Built on by (most cited first, 30 of 34)

- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [Contrastive Laplacian Eigenmaps](https://mlsanity.com/paper/2201.05493/) (2022)
- [NoisyMix: Boosting Model Robustness to Common Corruptions](https://mlsanity.com/paper/2202.01263/) (2022)
- [Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy](https://mlsanity.com/paper/2211.03796/) (2022)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning](https://mlsanity.com/paper/2107.11762/) (2021)
- [WOOD: Wasserstein-based Out-of-Distribution Detection](https://mlsanity.com/paper/2112.06384/) (2021)
- [AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data](https://mlsanity.com/paper/2107.06039/) (2021)
- [Turbo: Opportunistic Enhancement for Edge Video Analytics](https://mlsanity.com/paper/2207.00172/) (2022)
- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models](https://mlsanity.com/paper/2307.16865/) (2023)
- [Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks](https://mlsanity.com/paper/2107.14776/) (2021)
- [GAN-based Domain Inference Attack](https://mlsanity.com/paper/2212.11810/) (2022)
- [Switched Flow Matching: Eliminating Singularities via Switching ODEs](https://mlsanity.com/paper/2405.11605/) (2024)
- [Preserving logical and functional dependencies in synthetic tabular data](https://mlsanity.com/paper/2409.17684/) (2024)
- [The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC](https://mlsanity.com/paper/2105.14933/) (2021)
- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
- [Generative Adversarial Networks and Other Generative Models](https://mlsanity.com/paper/2207.03887/) (2022)
- [Structure-aware Fine-tuning for Code Pre-trained Models](https://mlsanity.com/paper/2404.07471/) (2024)
- [Stochastic full waveform inversion with deep generative prior for uncertainty quantification](https://mlsanity.com/paper/2406.04859/) (2024)
- [Diffusion prior as a direct regularization term for FWI](https://mlsanity.com/paper/2506.10141/) (2025)
- [Dependency-aware synthetic tabular data generation](https://mlsanity.com/paper/2507.19211/) (2025)
- [Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANs](https://mlsanity.com/paper/2601.05036/) (2026)
- [Regularized Sequential Latent Variable Models with Adversarial Neural Networks](https://mlsanity.com/paper/2108.04496/) (2021)
- [Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples](https://mlsanity.com/paper/2306.01206/) (2023)
- [Prediction of Diblock Copolymer Morphology via Machine Learning](https://mlsanity.com/paper/2308.16886/) (2023)
- [A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective](https://mlsanity.com/paper/2405.03052/) (2024)
- [FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning](https://mlsanity.com/paper/2104.08020/) (2021)
- [Wasserstein GAN: Deep Generation applied on Bitcoins financial time series](https://mlsanity.com/paper/2107.06008/) (2021)
- [Transfer Learning Based Efficient Traffic Prediction with Limited Training Data](https://mlsanity.com/paper/2205.04344/) (2022)

## 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.