{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/webvision-database/papers/ran/1","list_of":"/dataset/webvision-database","dataset":"WebVision","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,16],"of":16,"counts":{"papers_with_a_benchmark_row":51,"with_a_code_link":37,"where_syntology_ran_a_sample":16,"not_listed_spam_title":0,"listed":51,"listed_where_code_ran":16,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":5,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/webvision-database/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/label-retrieval-augmented-diffusion-models-1","slug":"label-retrieval-augmented-diffusion-models-1","title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","date":"2023-05-31","arxiv_id":"2305.19518","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["puar-playground/lra-diffusion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/label-retrieval-augmented-diffusion-models-1#ran","syntology_url":"https://syntology.ai/paper/2305.19518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19518"}}}},{"paper":"/paper/centrality-and-consistency-two-stage-clean","slug":"centrality-and-consistency-two-stage-clean","title":"Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels","date":"2022-07-29","arxiv_id":"2207.14476","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":3,"official":{"repos":["uitrbn/tscsi_idn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/centrality-and-consistency-two-stage-clean#ran","syntology_url":"https://syntology.ai/paper/2207.14476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14476"}}}},{"paper":"/paper/selective-supervised-contrastive-learning","slug":"selective-supervised-contrastive-learning","title":"Selective-Supervised Contrastive Learning with Noisy Labels","date":"2022-03-08","arxiv_id":"2203.04181","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["shikunli/sel-cl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/selective-supervised-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2203.04181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04181"}}}},{"paper":"/paper/cmw-net-learning-a-class-aware-sample","slug":"cmw-net-learning-a-class-aware-sample","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","date":"2022-02-11","arxiv_id":"2202.05613","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["xjtushujun/cmw-net"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cmw-net-learning-a-class-aware-sample#ran","syntology_url":"https://syntology.ai/paper/2202.05613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.05613"}}}},{"paper":"/paper/learning-with-neighbor-consistency-for-noisy-1","slug":"learning-with-neighbor-consistency-for-noisy-1","title":"Learning with Neighbor Consistency for Noisy Labels","date":"2022-02-04","arxiv_id":"2202.02200","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":4,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["google-research/scenic"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-with-neighbor-consistency-for-noisy-1#ran","syntology_url":"https://syntology.ai/paper/2202.02200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.02200"}}}},{"paper":"/paper/generalized-jensen-shannon-divergence-loss","slug":"generalized-jensen-shannon-divergence-loss","title":"Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels","date":"2021-05-10","arxiv_id":"2105.04522","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":1,"samples_ran_checked":2,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":5,"official":{"repos":["erikenglesson/gjs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/generalized-jensen-shannon-divergence-loss#ran","syntology_url":"https://syntology.ai/paper/2105.04522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04522"}}}},{"paper":"/paper/faster-meta-update-strategy-for-noise-robust","slug":"faster-meta-update-strategy-for-noise-robust","title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","date":"2021-04-30","arxiv_id":"2104.15092","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":3,"samples_constructed":1,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":7,"official":{"repos":["youjiangxu/FaMUS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/faster-meta-update-strategy-for-noise-robust#ran","syntology_url":"https://syntology.ai/paper/2104.15092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.15092"}}}},{"paper":"/paper/contrast-to-divide-self-supervised-pre-1","slug":"contrast-to-divide-self-supervised-pre-1","title":"Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels","date":"2021-03-25","arxiv_id":"2103.13646","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["ContrastToDivide/C2D"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/contrast-to-divide-self-supervised-pre-1#ran","syntology_url":"https://syntology.ai/paper/2103.13646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.13646"}}}},{"paper":"/paper/winning-ticket-in-noisy-image-classification","slug":"winning-ticket-in-noisy-image-classification","title":"FINE Samples for Learning with Noisy Labels","date":"2021-02-23","arxiv_id":"2102.11628","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":5,"samples_unverified":2,"pointer_only_for_licence":7,"official":{"repos":["Kthyeon/FINE_official"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/winning-ticket-in-noisy-image-classification#ran","syntology_url":"https://syntology.ai/paper/2102.11628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.11628"}}}},{"paper":"/paper/noisy-concurrent-training-for-efficient","slug":"noisy-concurrent-training-for-efficient","title":"Noisy Concurrent Training for Efficient Learning under Label Noise","date":"2020-09-17","arxiv_id":"2009.08325","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["NeurAI-Lab/NCT"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/noisy-concurrent-training-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2009.08325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08325"}}}},{"paper":"/paper/webly-supervised-image-classification-with","slug":"webly-supervised-image-classification-with","title":"Webly Supervised Image Classification with Self-Contained Confidence","date":"2020-08-27","arxiv_id":"2008.11894","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["bigvideoresearch/SCC","bigvideoresearch/Enigma"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/webly-supervised-image-classification-with#ran","syntology_url":"https://syntology.ai/paper/2008.11894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11894"}}}},{"paper":"/paper/early-learning-regularization-prevents","slug":"early-learning-regularization-prevents","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","date":"2020-06-30","arxiv_id":"2007.00151","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["shengliu66/ELR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/early-learning-regularization-prevents#ran","syntology_url":"https://syntology.ai/paper/2007.00151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.00151"}}}},{"paper":"/paper/normalized-loss-functions-for-deep-learning","slug":"normalized-loss-functions-for-deep-learning","title":"Normalized Loss Functions for Deep Learning with Noisy Labels","date":"2020-06-24","arxiv_id":"2006.13554","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["HanxunH/Active-Passive-Losses"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/normalized-loss-functions-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2006.13554","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.13554"}}}},{"paper":"/paper/understanding-and-utilizing-deep-neural","slug":"understanding-and-utilizing-deep-neural","title":"Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels","date":"2019-05-13","arxiv_id":"1905.05040","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["chenpf1025/noisy_label_understanding_utilizing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/understanding-and-utilizing-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1905.05040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.05040"}}}},{"paper":"/paper/co-teaching-robust-training-of-deep-neural","slug":"co-teaching-robust-training-of-deep-neural","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","date":"2018-04-18","arxiv_id":"1804.06872","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":6,"samples_unverified":0,"pointer_only_for_licence":7,"official":{"repos":["bhanML/Co-teaching"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/co-teaching-robust-training-of-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1804.06872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06872"}}}},{"paper":"/paper/making-deep-neural-networks-robust-to-label","slug":"making-deep-neural-networks-robust-to-label","title":"Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach","date":"2016-09-13","arxiv_id":"1609.03683","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["giorgiop/loss-correction"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/making-deep-neural-networks-robust-to-label#ran","syntology_url":"https://syntology.ai/paper/1609.03683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.03683"}}}}],"record_sha256":"800249785dfa1f371b47be9c1def3fc8d6fcda2a6469d0f96aff70d5f698ff32","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}