{"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/regularizing-class-wise-predictions-via-self","title":"Regularizing Class-wise Predictions via Self-knowledge Distillation","arxiv_id":"2003.13964","date":"2020-03-31","proceeding":"CVPR 2020 6","authors":["Sukmin Yun","Jongjin Park","Kimin Lee","Jinwoo Shin"],"abstract":"Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. 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