{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GFCTUTFKQNFAD7EQHTIHPPGX5U","short_pith_number":"pith:GFCTUTFK","schema_version":"1.0","canonical_sha256":"31453a4caa834a01fc903cd077bcd7ed150fb8b5e26c40a35f62320a76ad10de","source":{"kind":"arxiv","id":"2211.13993","version":3},"attestation_state":"computed","paper":{"title":"Combating noisy labels in object detection datasets","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adam Popowicz, Bart{\\l}omiej Olber, Jakub {\\L}yskawa, Krystian Chachu{\\l}a, Krystian Radlak, Piotr Fr\\k{a}tczak","submitted_at":"2022-11-25T10:05:06Z","abstract_excerpt":"The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as object detection. Dealing with errors in datasets is often limited to accepting that some fraction of examples are incorrect, estimating their confidence, and either assigning appropriate weights or ignoring uncertain ones during training. In this work, we propose a different approach. We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection dat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.13993","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-25T10:05:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f5fe9ec10be292b58beb4dd8d25f449d2ddf3085f7cc7acc99d7884f07bfd8e5","abstract_canon_sha256":"6ffaac6ded17d0ca4b1c24333b7c995617e3361f09834a8140355cf7d1d7fb05"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:14.323190Z","signature_b64":"7nAhY9Ozarh6vnkB7Kwb3Q752HXgi1qi5vzVkVRb1m73K4gTiLJ/PD2lFLB3b5q5XM6S1QgYYkvIkinSbADEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31453a4caa834a01fc903cd077bcd7ed150fb8b5e26c40a35f62320a76ad10de","last_reissued_at":"2026-07-05T07:22:14.322680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:14.322680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combating noisy labels in object detection datasets","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adam Popowicz, Bart{\\l}omiej Olber, Jakub {\\L}yskawa, Krystian Chachu{\\l}a, Krystian Radlak, Piotr Fr\\k{a}tczak","submitted_at":"2022-11-25T10:05:06Z","abstract_excerpt":"The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as object detection. Dealing with errors in datasets is often limited to accepting that some fraction of examples are incorrect, estimating their confidence, and either assigning appropriate weights or ignoring uncertain ones during training. In this work, we propose a different approach. We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13993","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.13993/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.13993","created_at":"2026-07-05T07:22:14.322739+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13993v3","created_at":"2026-07-05T07:22:14.322739+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13993","created_at":"2026-07-05T07:22:14.322739+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFCTUTFKQNFA","created_at":"2026-07-05T07:22:14.322739+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFCTUTFKQNFAD7EQ","created_at":"2026-07-05T07:22:14.322739+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFCTUTFK","created_at":"2026-07-05T07:22:14.322739+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02217","citing_title":"Understanding Trade offs When Conditioning Synthetic Data","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U","json":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U.json","graph_json":"https://pith.science/api/pith-number/GFCTUTFKQNFAD7EQHTIHPPGX5U/graph.json","events_json":"https://pith.science/api/pith-number/GFCTUTFKQNFAD7EQHTIHPPGX5U/events.json","paper":"https://pith.science/paper/GFCTUTFK"},"agent_actions":{"view_html":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U","download_json":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U.json","view_paper":"https://pith.science/paper/GFCTUTFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13993&json=true","fetch_graph":"https://pith.science/api/pith-number/GFCTUTFKQNFAD7EQHTIHPPGX5U/graph.json","fetch_events":"https://pith.science/api/pith-number/GFCTUTFKQNFAD7EQHTIHPPGX5U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U/action/storage_attestation","attest_author":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U/action/author_attestation","sign_citation":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U/action/citation_signature","submit_replication":"https://pith.science/pith/GFCTUTFKQNFAD7EQHTIHPPGX5U/action/replication_record"}},"created_at":"2026-07-05T07:22:14.322739+00:00","updated_at":"2026-07-05T07:22:14.322739+00:00"}