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From GAN to WGAN

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arxiv 1904.08994 v1 pith:XHIESDCS submitted 2019-04-18 cs.LG stat.ML

Lilian Weng This is my paper · ORCID
classification cs.LGstat.ML
keywords adoptingadversarialbehinddistancedistributionsexplainsgansgenerative
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Diffusion prior as a direct regularization term for FWI

    physics.geo-ph 2025-06 conditional novelty 5.0 of 10

    Using a pretrained DDPM denoiser as a score-rematching regularizer in FWI improves synthetic inversion stability and accuracy without running reverse diffusion sampling.

  2. Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity

    cs.NE 2024-12 conditional novelty 5.0 of 10

    A spectral-loss-regularized LSTM-CNN GAN generates synthetic eye-gaze velocity trajectories whose statistical moments and autocorrelation closely match real data, outperforming a four-state HMM in this comparison.

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