{"url":"/dataset/cifar-10n","name":"CIFAR-10N","full_name":"Real-World Human Annotations","description_markdown":"This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.","description_withheld":null,"homepage":"https://github.com/UCSC-REAL/cifar-10-100n","introduced_date":"2021-10-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-with-noisy-labels-revisited-a-study-1","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","first_author":"Jiaheng Wei","url":null},"license":null,"modalities":[],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Learning with noisy labels","url":"/task/learning-with-noisy-labels","datasets_with_task":"/datasets/task/learning-with-noisy-labels"}],"languages":[],"variants":["CIFAR-10N","CIFAR-10N-Aggregate","CIFAR-10N-Random1","CIFAR-10N-Random2","CIFAR-10N-Random3","CIFAR-10N-Worst"],"data_loaders":[{"repo":"https://github.com/ensta-u2is/torch-uncertainty","url":"https://torch-uncertainty.github.io/","frameworks":["pytorch"]},{"repo":"https://github.com/zwzhu-d/cifar-10-100n","url":"https://github.com/zwzhu-d/cifar-10-100n","frameworks":["pytorch"]}],"num_papers_in_archive":97,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N-Aggregate","rows":26,"metrics":["Accuracy (mean)"],"first_row_in_archive_order":{"model":"ProMix","paper":"/paper/promix-combating-label-noise-via-maximizing","metrics":{"Accuracy (mean)":"97.39"},"code_links":[{"title":"justherozen/promix","url":"https://github.com/justherozen/promix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-worst","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N-Worst","rows":25,"metrics":["Accuracy (mean)"],"first_row_in_archive_order":{"model":"ProMix","paper":"/paper/promix-combating-label-noise-via-maximizing","metrics":{"Accuracy (mean)":"96.16"},"code_links":[{"title":"justherozen/promix","url":"https://github.com/justherozen/promix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-1","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N-Random1","rows":24,"metrics":["Accuracy (mean)"],"first_row_in_archive_order":{"model":"ProMix","paper":"/paper/promix-combating-label-noise-via-maximizing","metrics":{"Accuracy (mean)":"96.97"},"code_links":[{"title":"justherozen/promix","url":"https://github.com/justherozen/promix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-2","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N-Random2","rows":23,"metrics":["Accuracy (mean)"],"first_row_in_archive_order":{"model":"PSSCL","paper":"/paper/psscl-a-progressive-sample-selection","metrics":{"Accuracy (mean)":"96.21"},"code_links":[{"title":"LanXiaoPang613/PSSCL","url":"https://github.com/LanXiaoPang613/PSSCL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-3","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N-Random3","rows":23,"metrics":["Accuracy (mean)"],"first_row_in_archive_order":{"model":"PSSCL","paper":"/paper/psscl-a-progressive-sample-selection","metrics":{"Accuracy (mean)":"96.49"},"code_links":[{"title":"LanXiaoPang613/PSSCL","url":"https://github.com/LanXiaoPang613/PSSCL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-cifar-10n-4","task":"Learning with noisy labels","dataset_variant":"CIFAR-10N","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ProMix","paper":"/paper/promix-combating-label-noise-via-maximizing","metrics":{"Accuracy":"97.39"},"code_links":[{"title":"justherozen/promix","url":"https://github.com/justherozen/promix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/psscl-a-progressive-sample-selection","title":"PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels","date":"2024-12-18","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/generative-noisy-label-learning-by-implicit","title":"Partial Label Supervision for Agnostic Generative Noisy Label Learning","date":"2023-08-02","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/imprecise-label-learning-a-unified-framework","title":"Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations","date":"2023-05-22","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/promix-combating-label-noise-via-maximizing","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","date":"2022-07-21","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/robust-training-under-label-noise-by-over","title":"Robust Training under Label Noise by Over-parameterization","date":"2022-02-28","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sample-prior-guided-robust-model-learning-to","title":"Sample Prior Guided Robust Model Learning to Suppress Noisy Labels","date":"2021-12-02","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/understanding-generalized-label-smoothing-1","title":"Understanding Generalized Label Smoothing when Learning with Noisy Labels","date":"2021-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/understanding-and-improving-early-stopping","title":"Understanding and Improving Early Stopping for Learning with Noisy Labels","date":"2021-06-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/understanding-generalized-label-smoothing","title":"To Smooth or Not? When Label Smoothing Meets Noisy Labels","date":"2021-06-08","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/clusterability-as-an-alternative-to-anchor","title":"Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels","date":"2021-02-10","rows_on_this_dataset":5,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/provably-end-to-end-label-noise-learning","title":"Provably End-to-end Label-Noise Learning without Anchor Points","date":"2021-02-04","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/when-optimizing-f-divergence-is-robust-with-1","title":"When Optimizing $f$-divergence is Robust with Label Noise","date":"2020-11-07","rows_on_this_dataset":5,"code_links":2,"syntology":null},{"paper":"/paper/learning-with-instance-dependent-label-noise-1","title":"Learning with Instance-Dependent Label Noise: A Sample Sieve Approach","date":"2020-10-05","rows_on_this_dataset":10,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/early-learning-regularization-prevents","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","date":"2020-06-30","rows_on_this_dataset":10,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/does-label-smoothing-mitigate-label-noise","title":"Does label smoothing mitigate label noise?","date":"2020-03-05","rows_on_this_dataset":5,"code_links":0,"syntology":null},{"paper":"/paper/combating-noisy-labels-by-agreement-a-joint","title":"Combating noisy labels by agreement: A joint training method with co-regularization","date":"2020-03-05","rows_on_this_dataset":5,"code_links":2,"syntology":null},{"paper":"/paper/dividemix-learning-with-noisy-labels-as-semi-1","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","date":"2020-02-18","rows_on_this_dataset":5,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/peer-loss-functions-learning-from-noisy","title":"Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates","date":"2019-10-08","rows_on_this_dataset":5,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/190600189","title":"Are Anchor Points Really Indispensable in Label-Noise Learning?","date":"2019-06-01","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/how-does-disagreement-help-generalization","title":"How does Disagreement Help Generalization against Label Corruption?","date":"2019-01-14","rows_on_this_dataset":5,"code_links":3,"syntology":null},{"paper":"/paper/generalized-cross-entropy-loss-for-training","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","date":"2018-05-20","rows_on_this_dataset":5,"code_links":4,"syntology":null},{"paper":"/paper/co-teaching-robust-training-of-deep-neural","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","date":"2018-04-18","rows_on_this_dataset":5,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/making-deep-neural-networks-robust-to-label","title":"Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach","date":"2016-09-13","rows_on_this_dataset":10,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":11,"samples_harvested":46,"samples_ran":23,"samples_unverified":23,"pointer_only_for_licence":21,"papers_with_no_sample_that_ran":3,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}