{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XC5XZDQICRIXNMP3ZWX5YQC6KF","short_pith_number":"pith:XC5XZDQI","schema_version":"1.0","canonical_sha256":"b8bb7c8e08145176b1fbcdafdc405e51697111621dbec658306ebc6ffdc63c4e","source":{"kind":"arxiv","id":"2408.16563","version":1},"attestation_state":"computed","paper":{"title":"MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ana F. Sequeira, Eduarda Caldeira, Jaime S. Cardoso, Pedro C. Neto","submitted_at":"2024-08-29T14:30:45Z","abstract_excerpt":"As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our res"},"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":"2408.16563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-29T14:30:45Z","cross_cats_sorted":[],"title_canon_sha256":"0f24c5199363fed8ac619d3c8fadde5fe7a8f536ea4a7f5bd996100e6f9e8b67","abstract_canon_sha256":"82a3963167b49f94b3bea072f27c5e763e5e602a51c5c19aeca0730ad78e47e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:43.154671Z","signature_b64":"IxmHLAfBu29SPfDIo8PO2v89NQVLWSwKy30GPXDbt8xBPQECKBhsNBYhwnOqvVaiYcY845lYNxfJ9W7BEdkTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8bb7c8e08145176b1fbcdafdc405e51697111621dbec658306ebc6ffdc63c4e","last_reissued_at":"2026-07-05T09:00:43.154136Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:43.154136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ana F. Sequeira, Eduarda Caldeira, Jaime S. Cardoso, Pedro C. Neto","submitted_at":"2024-08-29T14:30:45Z","abstract_excerpt":"As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teacher framework to distill knowledge to a student network. In our approach, directed at face recognition use cases, we train four teachers on one specific ethnicity, leading to four highly specialized and biased teachers. Our strategy learns a project of these four teachers into a common space and distill that information to a student network. Our res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.16563","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/2408.16563/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":"2408.16563","created_at":"2026-07-05T09:00:43.154193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.16563v1","created_at":"2026-07-05T09:00:43.154193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.16563","created_at":"2026-07-05T09:00:43.154193+00:00"},{"alias_kind":"pith_short_12","alias_value":"XC5XZDQICRIX","created_at":"2026-07-05T09:00:43.154193+00:00"},{"alias_kind":"pith_short_16","alias_value":"XC5XZDQICRIXNMP3","created_at":"2026-07-05T09:00:43.154193+00:00"},{"alias_kind":"pith_short_8","alias_value":"XC5XZDQI","created_at":"2026-07-05T09:00:43.154193+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01532","citing_title":"Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognition","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF","json":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF.json","graph_json":"https://pith.science/api/pith-number/XC5XZDQICRIXNMP3ZWX5YQC6KF/graph.json","events_json":"https://pith.science/api/pith-number/XC5XZDQICRIXNMP3ZWX5YQC6KF/events.json","paper":"https://pith.science/paper/XC5XZDQI"},"agent_actions":{"view_html":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF","download_json":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF.json","view_paper":"https://pith.science/paper/XC5XZDQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.16563&json=true","fetch_graph":"https://pith.science/api/pith-number/XC5XZDQICRIXNMP3ZWX5YQC6KF/graph.json","fetch_events":"https://pith.science/api/pith-number/XC5XZDQICRIXNMP3ZWX5YQC6KF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF/action/storage_attestation","attest_author":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF/action/author_attestation","sign_citation":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF/action/citation_signature","submit_replication":"https://pith.science/pith/XC5XZDQICRIXNMP3ZWX5YQC6KF/action/replication_record"}},"created_at":"2026-07-05T09:00:43.154193+00:00","updated_at":"2026-07-05T09:00:43.154193+00:00"}