{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:G5Z7P6BLL2KXSKEMWIPUCE76LQ","short_pith_number":"pith:G5Z7P6BL","schema_version":"1.0","canonical_sha256":"3773f7f82b5e9579288cb21f4113fe5c239f46261c0b8397e11161db741e60bd","source":{"kind":"arxiv","id":"2003.10471","version":1},"attestation_state":"computed","paper":{"title":"Label Noise Types and Their Effects on Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"G\\\"orkem Algan, \\.Ilkay Ulusoy","submitted_at":"2020-03-23T18:03:39Z","abstract_excerpt":"The recent success of deep learning is mostly due to the availability of big datasets with clean annotations. However, gathering a cleanly annotated dataset is not always feasible due to practical challenges. As a result, label noise is a common problem in datasets, and numerous methods to train deep neural networks in the presence of noisy labels are proposed in the literature. These methods commonly use benchmark datasets with synthetic label noise on the training set. However, there are multiple types of label noise, and each of them has its own characteristic impact on learning. Since each"},"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":"2003.10471","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-03-23T18:03:39Z","cross_cats_sorted":[],"title_canon_sha256":"2921a3ca5e75527714490a0200b287e16e36fa7ec336dde2262c49ab7b146ab1","abstract_canon_sha256":"f042d9b74e509b9c17eb50f093123b75ca0387b2ceab6b6a04ca15c445db40d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:53.321907Z","signature_b64":"9AzlWkz+wo3NtJ7/IKoDB1jYJZYqBA3pilfZq+8/ZF+SvvOj5ekLNIqEOJnnr3zWKbUi7I/PEoJ7gbrCThzmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3773f7f82b5e9579288cb21f4113fe5c239f46261c0b8397e11161db741e60bd","last_reissued_at":"2026-07-05T00:49:53.321430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:53.321430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Label Noise Types and Their Effects on Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"G\\\"orkem Algan, \\.Ilkay Ulusoy","submitted_at":"2020-03-23T18:03:39Z","abstract_excerpt":"The recent success of deep learning is mostly due to the availability of big datasets with clean annotations. However, gathering a cleanly annotated dataset is not always feasible due to practical challenges. As a result, label noise is a common problem in datasets, and numerous methods to train deep neural networks in the presence of noisy labels are proposed in the literature. These methods commonly use benchmark datasets with synthetic label noise on the training set. However, there are multiple types of label noise, and each of them has its own characteristic impact on learning. Since each"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.10471","kind":"arxiv","version":1},"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/2003.10471/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":"2003.10471","created_at":"2026-07-05T00:49:53.321488+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.10471v1","created_at":"2026-07-05T00:49:53.321488+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.10471","created_at":"2026-07-05T00:49:53.321488+00:00"},{"alias_kind":"pith_short_12","alias_value":"G5Z7P6BLL2KX","created_at":"2026-07-05T00:49:53.321488+00:00"},{"alias_kind":"pith_short_16","alias_value":"G5Z7P6BLL2KXSKEM","created_at":"2026-07-05T00:49:53.321488+00:00"},{"alias_kind":"pith_short_8","alias_value":"G5Z7P6BL","created_at":"2026-07-05T00:49:53.321488+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04029","citing_title":"Securing Contrastive mmWave-based Human Activity Recognition against Adversarial Label Flipping","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ","json":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ.json","graph_json":"https://pith.science/api/pith-number/G5Z7P6BLL2KXSKEMWIPUCE76LQ/graph.json","events_json":"https://pith.science/api/pith-number/G5Z7P6BLL2KXSKEMWIPUCE76LQ/events.json","paper":"https://pith.science/paper/G5Z7P6BL"},"agent_actions":{"view_html":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ","download_json":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ.json","view_paper":"https://pith.science/paper/G5Z7P6BL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.10471&json=true","fetch_graph":"https://pith.science/api/pith-number/G5Z7P6BLL2KXSKEMWIPUCE76LQ/graph.json","fetch_events":"https://pith.science/api/pith-number/G5Z7P6BLL2KXSKEMWIPUCE76LQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ/action/storage_attestation","attest_author":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ/action/author_attestation","sign_citation":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ/action/citation_signature","submit_replication":"https://pith.science/pith/G5Z7P6BLL2KXSKEMWIPUCE76LQ/action/replication_record"}},"created_at":"2026-07-05T00:49:53.321488+00:00","updated_at":"2026-07-05T00:49:53.321488+00:00"}