{"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-neural-networks-by-penalizing","title":"Regularizing Neural Networks by Penalizing Confident Output Distributions","arxiv_id":"1701.06548","date":"2017-01-23","proceeding":null,"authors":["Gabriel Pereyra","George Tucker","Jan Chorowski","Łukasz Kaiser","Geoffrey Hinton"],"abstract":"We systematically explore regularizing neural networks by penalizing low\nentropy output distributions. We show that penalizing low entropy output\ndistributions, which has been shown to improve exploration in reinforcement\nlearning, acts as a strong regularizer in supervised learning. Furthermore, we\nconnect a maximum entropy based confidence penalty to label smoothing through\nthe direction of the KL divergence. We exhaustively evaluate the proposed\nconfidence penalty and label smoothing on 6 common benchmarks: image\nclassification (MNIST and Cifar-10), language modeling (Penn Treebank), machine\ntranslation (WMT'14 English-to-German), and speech recognition (TIMIT and WSJ).\nWe find that both label smoothing and the confidence penalty improve\nstate-of-the-art models across benchmarks without modifying existing\nhyperparameters, suggesting the wide applicability of these regularizers.","url_abs":"http://arxiv.org/abs/1701.06548v1","url_pdf":"http://arxiv.org/pdf/1701.06548v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"regularizing-neural-networks-by-penalizing","repo_url":"https://github.com/hyang0129/foodclassapp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"regularizing-neural-networks-by-penalizing","repo_url":"https://github.com/makeyourownmaker/mixup","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"label-smoothing","method_name":"Label Smoothing"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.06548","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}