{"url":"/dataset/red-miniimagenet-40-label-noise","name":"Red MiniImageNet 40% label noise","full_name":null,"description_markdown":"Part of the Controlled Noisy Web Labels Dataset.","description_withheld":null,"homepage":"https://www.tensorflow.org/datasets/catalog/controlled_noisy_web_labels","introduced_date":"2019-11-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/synthetic-vs-real-deep-learning-on-controlled-1","title":"Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels","first_author":"Lu Jiang","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":["Red MiniImageNet 40% label noise"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-red-miniimagenet-40","task":"Image Classification","dataset_variant":"Red MiniImageNet 40% label noise","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NCR (ResNet-18)","paper":"/paper/learning-with-neighbor-consistency-for-noisy-1","metrics":{"Accuracy":"64.6"},"code_links":[{"title":"google-research/scenic","url":"https://github.com/google-research/scenic"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-red-1","task":"Learning with noisy labels","dataset_variant":"Red MiniImageNet 40% label noise","rows":4,"metrics":["Test Accuracy"],"first_row_in_archive_order":{"model":"NCR (ResNet-18)","paper":"/paper/learning-with-neighbor-consistency-for-noisy-1","metrics":{"Test Accuracy":"64.6"},"code_links":[{"title":"google-research/scenic","url":"https://github.com/google-research/scenic"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/clipcleaner-cleaning-noisy-labels-with-clip","title":"CLIPCleaner: Cleaning Noisy Labels with CLIP","date":"2024-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/instance-dependent-noisy-label-learning-via","title":"Instance-Dependent Noisy Label Learning via Graphical Modelling","date":"2022-09-02","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/learning-with-neighbor-consistency-for-noisy-1","title":"Learning with Neighbor Consistency for Noisy Labels","date":"2022-02-04","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/propmix-hard-sample-filtering-and","title":"PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels","date":"2021-10-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/faster-meta-update-strategy-for-noise-robust","title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","date":"2021-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":2,"samples_unverified":5,"pointer_only_for_licence":7,"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":2,"samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":7,"papers_with_no_sample_that_ran":0,"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."}