{"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/probabilistic-end-to-end-noise-correction-for","title":"Probabilistic End-to-end Noise Correction for Learning with Noisy Labels","arxiv_id":"1903.07788","date":"2019-03-19","proceeding":"CVPR 2019 6","authors":["Kun Yi","Jianxin Wu"],"abstract":"Deep learning has achieved excellent performance in various computer vision\ntasks, but requires a lot of training examples with clean labels. It is easy to\ncollect a dataset with noisy labels, but such noise makes networks overfit\nseriously and accuracies drop dramatically. To address this problem, we propose\nan end-to-end framework called PENCIL, which can update both network parameters\nand label estimations as label distributions. PENCIL is independent of the\nbackbone network structure and does not need an auxiliary clean dataset or\nprior information about noise, thus it is more general and robust than existing\nmethods and is easy to apply. PENCIL outperforms previous state-of-the-art\nmethods by large margins on both synthetic and real-world datasets with\ndifferent noise types and noise rates. Experiments show that PENCIL is robust\non clean datasets, too.","url_abs":"http://arxiv.org/abs/1903.07788v1","url_pdf":"http://arxiv.org/pdf/1903.07788v1.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":"probabilistic-end-to-end-noise-correction-for","repo_url":"https://github.com/yikun2019/PENCIL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"probabilistic-end-to-end-noise-correction-for","repo_url":"https://github.com/JacobPfau/PENCIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"probabilistic-end-to-end-noise-correction-for","repo_url":"https://github.com/ljmiao/PENCIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-with-noisy-labels","task_name":"Learning with noisy labels"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-clothing1m","task":"Image Classification","dataset":"Clothing1M","model":"PENCIL","rank_in_archive_order":26,"of":51,"metrics":{"Accuracy":"73.49%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.07788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07788"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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