{"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/ieg-robust-neural-network-training-to-tackle","title":"Distilling Effective Supervision from Severe Label Noise","arxiv_id":"1910.00701","date":"2019-10-01","proceeding":"CVPR 2020 6","authors":["Zizhao Zhang","Han Zhang","Sercan O. Arik","Honglak Lee","Tomas Pfister"],"abstract":"Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer a lot from label noise. This paper targets at the challenge of robust training at high label noise regimes. The key insight to achieve this goal is to wisely leverage a small trusted set to estimate exemplar weights and pseudo labels for noisy data in order to reuse them for supervised training. We present a holistic framework to train deep neural networks in a way that is highly invulnerable to label noise. Our method sets the new state of the art on various types of label noise and achieves excellent performance on large-scale datasets with real-world label noise. For instance, on CIFAR100 with a $40\\%$ uniform noise ratio and only 10 trusted labeled data per class, our method achieves $80.2{\\pm}0.3\\%$ classification accuracy, where the error rate is only $1.4\\%$ higher than a neural network trained without label noise. Moreover, increasing the noise ratio to $80\\%$, our method still maintains a high accuracy of $75.5{\\pm}0.2\\%$, compared to the previous best accuracy $48.2\\%$. Source code available: https://github.com/google-research/google-research/tree/master/ieg","url_abs":"https://arxiv.org/abs/1910.00701v5","url_pdf":"https://arxiv.org/pdf/1910.00701v5.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":"ieg-robust-neural-network-training-to-tackle","repo_url":"https://github.com/google-research/google-research","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"ieg-robust-neural-network-training-to-tackle","repo_url":"https://github.com/google-research/google-research/tree/master/ieg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}