{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GWWDS5FSKZPQ3CR2AGFCXI3NGI","short_pith_number":"pith:GWWDS5FS","schema_version":"1.0","canonical_sha256":"35ac3974b2565f0d8a3a018a2ba36d32084755179b10a483a728a8bc50b52a41","source":{"kind":"arxiv","id":"1803.00839","version":1},"attestation_state":"computed","paper":{"title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Cheng Li, Kaidi Cao, Xiaoou Tang, Yu Rong","submitted_at":"2018-03-02T13:25:34Z","abstract_excerpt":"Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces. A key reason is that the number of frontal and profile training faces are highly imbalanced - there are extensively more frontal training samples compared to profile ones. In addition, it is intrinsically hard to learn a deep representation that is geometrically invariant to large pose variations. In this study, we hypothesize that there is an inherent mapping between front"},"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":"1803.00839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-02T13:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"677ab828a59ed9db35b5786617a8a6d3ae733c9950d80afcee31fd56c2ba14fc","abstract_canon_sha256":"0d498edd7492a9ccb405d50a65ca962b70ad1bae0311c4ff08eb04f53fe468f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:07.701891Z","signature_b64":"8mG6oD9f21dlwym4UaekQTdZKs6hJS0epXHg96aMRA1PRKqIth4cU+FZG2JVT5FnDqCST2ESeANPPom0a1DNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35ac3974b2565f0d8a3a018a2ba36d32084755179b10a483a728a8bc50b52a41","last_reissued_at":"2026-05-18T00:22:07.701303Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:07.701303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Cheng Li, Kaidi Cao, Xiaoou Tang, Yu Rong","submitted_at":"2018-03-02T13:25:34Z","abstract_excerpt":"Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces. A key reason is that the number of frontal and profile training faces are highly imbalanced - there are extensively more frontal training samples compared to profile ones. In addition, it is intrinsically hard to learn a deep representation that is geometrically invariant to large pose variations. In this study, we hypothesize that there is an inherent mapping between front"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00839","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":""},"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":"1803.00839","created_at":"2026-05-18T00:22:07.701404+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.00839v1","created_at":"2026-05-18T00:22:07.701404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00839","created_at":"2026-05-18T00:22:07.701404+00:00"},{"alias_kind":"pith_short_12","alias_value":"GWWDS5FSKZPQ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GWWDS5FSKZPQ3CR2","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GWWDS5FS","created_at":"2026-05-18T12:32:25.280505+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/GWWDS5FSKZPQ3CR2AGFCXI3NGI","json":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI.json","graph_json":"https://pith.science/api/pith-number/GWWDS5FSKZPQ3CR2AGFCXI3NGI/graph.json","events_json":"https://pith.science/api/pith-number/GWWDS5FSKZPQ3CR2AGFCXI3NGI/events.json","paper":"https://pith.science/paper/GWWDS5FS"},"agent_actions":{"view_html":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI","download_json":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI.json","view_paper":"https://pith.science/paper/GWWDS5FS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.00839&json=true","fetch_graph":"https://pith.science/api/pith-number/GWWDS5FSKZPQ3CR2AGFCXI3NGI/graph.json","fetch_events":"https://pith.science/api/pith-number/GWWDS5FSKZPQ3CR2AGFCXI3NGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI/action/storage_attestation","attest_author":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI/action/author_attestation","sign_citation":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI/action/citation_signature","submit_replication":"https://pith.science/pith/GWWDS5FSKZPQ3CR2AGFCXI3NGI/action/replication_record"}},"created_at":"2026-05-18T00:22:07.701404+00:00","updated_at":"2026-05-18T00:22:07.701404+00:00"}