{"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/joint-optimization-framework-for-learning","title":"Joint Optimization Framework for Learning with Noisy Labels","arxiv_id":"1803.11364","date":"2018-03-30","proceeding":"CVPR 2018 6","authors":["Daiki Tanaka","Daiki Ikami","Toshihiko Yamasaki","Kiyoharu Aizawa"],"abstract":"Deep neural networks (DNNs) trained on large-scale datasets have exhibited\nsignificant performance in image classification. Many large-scale datasets are\ncollected from websites, however they tend to contain inaccurate labels that\nare termed as noisy labels. Training on such noisy labeled datasets causes\nperformance degradation because DNNs easily overfit to noisy labels. To\novercome this problem, we propose a joint optimization framework of learning\nDNN parameters and estimating true labels. Our framework can correct labels\nduring training by alternating update of network parameters and labels. We\nconduct experiments on the noisy CIFAR-10 datasets and the Clothing1M dataset.\nThe results indicate that our approach significantly outperforms other\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1803.11364v1","url_pdf":"http://arxiv.org/pdf/1803.11364v1.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":"joint-optimization-framework-for-learning","repo_url":"https://github.com/DaikiTanaka-UT/JointOptimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-with-noisy-labels","task_name":"Learning with noisy labels"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-clothing1m","task":"Image Classification","dataset":"Clothing1M","model":"Joint Opt.","rank_in_archive_order":40,"of":51,"metrics":{"Accuracy":"72.23%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11364","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}