{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5K73H7O5ITT6BGW3GG4KKHJ6XG","short_pith_number":"pith:5K73H7O5","schema_version":"1.0","canonical_sha256":"eabfb3fddd44e7e09adb31b8a51d3eb9ac16a2f0eba31014ed55787725cf5672","source":{"kind":"arxiv","id":"1808.03114","version":4},"attestation_state":"computed","paper":{"title":"Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alex B\\\"auerle, Heiko Neumann, Timo Ropinski","submitted_at":"2018-08-09T12:34:33Z","abstract_excerpt":"Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training d"},"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":"1808.03114","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-09T12:34:33Z","cross_cats_sorted":["cs.HC","cs.LG"],"title_canon_sha256":"97921a1c79cfa09724ef678083aef16f599286635fb0f46ced70e9dbf275681d","abstract_canon_sha256":"c79f16cbb70b0052ef1c7cc774188229dccdfedb14a3817f343466e2a96568b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:58.126443Z","signature_b64":"i0DVEgT7G1ddVthNVG31C/fGC2UuUD9igf4BF9JislVLiY85H/htggB7otioEepS72dTOOx7SRvcl2cdD+IvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eabfb3fddd44e7e09adb31b8a51d3eb9ac16a2f0eba31014ed55787725cf5672","last_reissued_at":"2026-07-05T01:08:58.125990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:58.125990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alex B\\\"auerle, Heiko Neumann, Timo Ropinski","submitted_at":"2018-08-09T12:34:33Z","abstract_excerpt":"Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.03114","kind":"arxiv","version":4},"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/1808.03114/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":"1808.03114","created_at":"2026-07-05T01:08:58.126062+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.03114v4","created_at":"2026-07-05T01:08:58.126062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.03114","created_at":"2026-07-05T01:08:58.126062+00:00"},{"alias_kind":"pith_short_12","alias_value":"5K73H7O5ITT6","created_at":"2026-07-05T01:08:58.126062+00:00"},{"alias_kind":"pith_short_16","alias_value":"5K73H7O5ITT6BGW3","created_at":"2026-07-05T01:08:58.126062+00:00"},{"alias_kind":"pith_short_8","alias_value":"5K73H7O5","created_at":"2026-07-05T01:08:58.126062+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG","json":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG.json","graph_json":"https://pith.science/api/pith-number/5K73H7O5ITT6BGW3GG4KKHJ6XG/graph.json","events_json":"https://pith.science/api/pith-number/5K73H7O5ITT6BGW3GG4KKHJ6XG/events.json","paper":"https://pith.science/paper/5K73H7O5"},"agent_actions":{"view_html":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG","download_json":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG.json","view_paper":"https://pith.science/paper/5K73H7O5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.03114&json=true","fetch_graph":"https://pith.science/api/pith-number/5K73H7O5ITT6BGW3GG4KKHJ6XG/graph.json","fetch_events":"https://pith.science/api/pith-number/5K73H7O5ITT6BGW3GG4KKHJ6XG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG/action/storage_attestation","attest_author":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG/action/author_attestation","sign_citation":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG/action/citation_signature","submit_replication":"https://pith.science/pith/5K73H7O5ITT6BGW3GG4KKHJ6XG/action/replication_record"}},"created_at":"2026-07-05T01:08:58.126062+00:00","updated_at":"2026-07-05T01:08:58.126062+00:00"}