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Nonetheless, recent studies on the\nmemorization effects of deep neural networks show that they would first\nmemorize training data of clean labels and then those of noisy labels.\nTherefore in this paper, we propose a new deep learning paradigm called\nCo-teaching for combating with noisy labels. Namely, we train two deep neural\nnetworks simultaneously, and let them teach each other given every mini-batch:\nfirstly, each network feeds forward all data and selects some data of possibly\nclean labels; secondly, two networks communicate with each other what data in\nthis mini-batch should be used for training; finally, each network back\npropagates the data selected by its peer network and updates itself. Empirical\nresults on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that\nCo-teaching is much superior to the state-of-the-art methods in the robustness\nof trained deep models.","url_abs":"http://arxiv.org/abs/1804.06872v3","url_pdf":"http://arxiv.org/pdf/1804.06872v3.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":"co-teaching-robust-training-of-deep-neural","repo_url":"https://github.com/bhanML/Co-teaching","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"co-teaching-robust-training-of-deep-neural","repo_url":"https://github.com/smilelab-fl/fednoisy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"co-teaching-robust-training-of-deep-neural","repo_url":"https://github.com/viethungluu/co-teaching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"co-teaching-robust-training-of-deep-neural","repo_url":"https://github.com/yeachan-kr/pytorch-coteaching","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"co-teaching-robust-training-of-deep-neural","repo_url":"https://github.com/ziegler-ingo/cleavage_prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-with-noisy-labels","task_name":"Learning with noisy labels"},{"task_slug":"memorization","task_name":"Memorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-clothing1m","task":"Image Classification","dataset":"Clothing1M","model":"CoT","rank_in_archive_order":48,"of":51,"metrics":{"Accuracy":"70.15%"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-mini-webvision-1-0","task":"Image Classification","dataset":"mini WebVision 1.0","model":"Co-teaching (Inception-ResNet-v2)","rank_in_archive_order":40,"of":47,"metrics":{"ImageNet Top-1 Accuracy":"61.48","ImageNet Top-5 Accuracy":"84.70","Top-1 Accuracy":"63.58","Top-5 Accuracy":"85.20"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-100n","task":"Learning with noisy labels","dataset":"CIFAR-100N","model":"Co-Teaching","rank_in_archive_order":10,"of":24,"metrics":{"Accuracy (mean)":"60.37"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n","task":"Learning with noisy labels","dataset":"CIFAR-10N-Aggregate","model":"Co-Teaching","rank_in_archive_order":18,"of":26,"metrics":{"Accuracy (mean)":"91.20"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-1","task":"Learning with noisy labels","dataset":"CIFAR-10N-Random1","model":"Co-Teaching","rank_in_archive_order":11,"of":24,"metrics":{"Accuracy (mean)":"90.33"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-2","task":"Learning with noisy labels","dataset":"CIFAR-10N-Random2","model":"Co-Teaching","rank_in_archive_order":11,"of":23,"metrics":{"Accuracy (mean)":"90.30"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-3","task":"Learning with noisy labels","dataset":"CIFAR-10N-Random3","model":"Co-Teaching","rank_in_archive_order":9,"of":23,"metrics":{"Accuracy (mean)":"90.15"},"uses_additional_data":false},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-worst","task":"Learning with noisy labels","dataset":"CIFAR-10N-Worst","model":"Co-Teaching","rank_in_archive_order":11,"of":25,"metrics":{"Accuracy (mean)":"83.83"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.06872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06872"}},"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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