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Wasserstein GAN

WGAN

95 papers tagged archive 2025-07-28

Introduced by Martin Arjovsky et al. in Wasserstein GAN

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSourceSee Code · daheyinyin/wgan

Papers archive 2025-07-28

30 shown of 95, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 85 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Generative Adversarial Network24
Image Generation15
Denoising7
Data Augmentation4
Diversity4
Image-to-Image Translation4
Time Series4
Time Series Analysis4
Synthetic Data Generation3
Translation3
CPU2
Decoder2
EEG2
General Classification2
Image Denoising2
Image Reconstruction2
Management2
SSIM2
Super-Resolution2
Transfer Learning2

Usage over time archive 2025-07-28

Papers per year tagged with WGAN: 2017 to 2025, peak 18 18 0 2017: 8 papers 2017 2018: 16 papers 2018 2019: 18 papers 2019 2020: 13 papers 2020 2021: 18 papers 2021 2022: 9 papers 2022 2023: 4 papers 2023 2024: 8 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (95 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Adversarial Networks

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