{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improved-training-of-wasserstein-gans","title":"Improved Training of Wasserstein GANs","arxiv_id":"1704.00028","date":"2017-03-31","proceeding":"NeurIPS 2017 12","authors":["Ishaan Gulrajani","Faruk Ahmed","Martin Arjovsky","Vincent Dumoulin","Aaron Courville"],"abstract":"Generative Adversarial Networks (GANs) are powerful generative models, but\nsuffer from training instability. The recently proposed Wasserstein GAN (WGAN)\nmakes progress toward stable training of GANs, but sometimes can still generate\nonly low-quality samples or fail to converge. We find that these problems are\noften due to the use of weight clipping in WGAN to enforce a Lipschitz\nconstraint on the critic, which can lead to undesired behavior. We propose an\nalternative to clipping weights: penalize the norm of gradient of the critic\nwith respect to its input. Our proposed method performs better than standard\nWGAN and enables stable training of a wide variety of GAN architectures with\nalmost no hyperparameter tuning, including 101-layer ResNets and language\nmodels over discrete data. We also achieve high quality generations on CIFAR-10\nand LSUN bedrooms.","url_abs":"http://arxiv.org/abs/1704.00028v3","url_pdf":"http://arxiv.org/pdf/1704.00028v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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