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      "statement": "# Wasserstein GAN and gradient-penalty training\n\nUsed 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.\n\n## Scientific scope\n\nWasserstein 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.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMartin Arjovsky; Soumith Chintala; Léon Bottou; Ishaan Gulrajani; Faruk Ahmed; Vincent Dumoulin; Aaron Courville\n\nSource-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.\n\n\n\n## Original sources\n\n- [Wasserstein GAN](https://arxiv.org/abs/1701.07875v1)\n- [Improved Training of Wasserstein GANs](https://arxiv.org/abs/1704.00028v1)\n",
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