{"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/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","arxiv_id":"1710.09412","date":"2017-10-25","proceeding":"ICLR 2018 1","authors":["Hongyi Zhang","Moustapha Cisse","Yann N. Dauphin","David Lopez-Paz"],"abstract":"Large deep neural networks are powerful, but exhibit undesirable behaviors\nsuch as memorization and sensitivity to adversarial examples. In this work, we\npropose mixup, a simple learning principle to alleviate these issues. In\nessence, mixup trains a neural network on convex combinations of pairs of\nexamples and their labels. By doing so, mixup regularizes the neural network to\nfavor simple linear behavior in-between training examples. Our experiments on\nthe ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show\nthat mixup improves the generalization of state-of-the-art neural network\narchitectures. We also find that mixup reduces the memorization of corrupt\nlabels, increases the robustness to adversarial examples, and stabilizes the\ntraining of generative adversarial networks.","url_abs":"http://arxiv.org/abs/1710.09412v2","url_pdf":"http://arxiv.org/pdf/1710.09412v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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