{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AZUXRNINYFXOLYGUZ5CHPXD43X","short_pith_number":"pith:AZUXRNIN","schema_version":"1.0","canonical_sha256":"066978b50dc16ee5e0d4cf4477dc7cddf251549a1ccdf9ace6d2f5cb5e28e127","source":{"kind":"arxiv","id":"2310.19545","version":3},"attestation_state":"computed","paper":{"title":"MENTOR: Human Perception-Guided Pretraining for Increased Generalization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Czajka, Colton R. Crum","submitted_at":"2023-10-30T13:50:44Z","abstract_excerpt":"Leveraging human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture or training pipeline) and how to efficiently incorporate always limited human perceptual data into training strategies of models. In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization), which addresses this question through two unique rounds of training CNNs tasked with open-set anomaly detection. First, we trai"},"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":"2310.19545","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-30T13:50:44Z","cross_cats_sorted":[],"title_canon_sha256":"69ee6adcf01f19f7188f7cdf271855e3130e470449b543e510e4c18c28f2b5b4","abstract_canon_sha256":"edb147b494e737f8a7f18692dfe49671b3f1166c439b58a36729ce2a2878cef3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:10.736520Z","signature_b64":"ZOpiun5EtiEl6WfBWkjzEWim7c4HGVczNaNY+UJh/6+UDHejIRi+V9sa3McOeROuCOrlyd98DAHb5g0Y/MMFDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"066978b50dc16ee5e0d4cf4477dc7cddf251549a1ccdf9ace6d2f5cb5e28e127","last_reissued_at":"2026-07-05T10:54:10.735871Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:10.735871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MENTOR: Human Perception-Guided Pretraining for Increased Generalization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Czajka, Colton R. Crum","submitted_at":"2023-10-30T13:50:44Z","abstract_excerpt":"Leveraging human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture or training pipeline) and how to efficiently incorporate always limited human perceptual data into training strategies of models. In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization), which addresses this question through two unique rounds of training CNNs tasked with open-set anomaly detection. First, we trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19545","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/2310.19545/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":"2310.19545","created_at":"2026-07-05T10:54:10.735956+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.19545v3","created_at":"2026-07-05T10:54:10.735956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19545","created_at":"2026-07-05T10:54:10.735956+00:00"},{"alias_kind":"pith_short_12","alias_value":"AZUXRNINYFXO","created_at":"2026-07-05T10:54:10.735956+00:00"},{"alias_kind":"pith_short_16","alias_value":"AZUXRNINYFXOLYGU","created_at":"2026-07-05T10:54:10.735956+00:00"},{"alias_kind":"pith_short_8","alias_value":"AZUXRNIN","created_at":"2026-07-05T10:54:10.735956+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.05553","citing_title":"Psych-Occlusion: Using Visual Psychophysics for Aerial Detection of Occluded Persons during Search and Rescue","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X","json":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X.json","graph_json":"https://pith.science/api/pith-number/AZUXRNINYFXOLYGUZ5CHPXD43X/graph.json","events_json":"https://pith.science/api/pith-number/AZUXRNINYFXOLYGUZ5CHPXD43X/events.json","paper":"https://pith.science/paper/AZUXRNIN"},"agent_actions":{"view_html":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X","download_json":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X.json","view_paper":"https://pith.science/paper/AZUXRNIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.19545&json=true","fetch_graph":"https://pith.science/api/pith-number/AZUXRNINYFXOLYGUZ5CHPXD43X/graph.json","fetch_events":"https://pith.science/api/pith-number/AZUXRNINYFXOLYGUZ5CHPXD43X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X/action/storage_attestation","attest_author":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X/action/author_attestation","sign_citation":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X/action/citation_signature","submit_replication":"https://pith.science/pith/AZUXRNINYFXOLYGUZ5CHPXD43X/action/replication_record"}},"created_at":"2026-07-05T10:54:10.735956+00:00","updated_at":"2026-07-05T10:54:10.735956+00:00"}