AWhat changed
Used a Lipschitz-constrained critic motivated by Wasserstein-1 duality, then replaced restrictive weight clipping with a sampled input-gradient penalty to improve practical adversarial training.
Scientific scope
Used a Lipschitz-constrained critic motivated by Wasserstein-1 duality, then replaced restrictive weight clipping with a sampled input-gradient penalty to improve practical adversarial training. Wasserstein duality is inherited mathematics. A finite-capacity imperfect critic and sampled soft penalty do not compute the exact distance or enforce global1-Lipschitzness. Optimal-critic theory does not prove convergence of every practical GAN; training gains and failure modes are source-reported.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Wasserstein duality is inherited mathematics. A finite-capacity imperfect critic and sampled soft penalty do not compute the exact distance or enforce global1-Lipschitzness. Optimal-critic theory does not prove convergence of every practical GAN; training gains and failure modes are source-reported.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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