{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7OGIILJARBYHEGSHRHWLOLVEQ7","short_pith_number":"pith:7OGIILJA","schema_version":"1.0","canonical_sha256":"fb8c842d208870721a4789ecb72ea487f54eceb2a5c503da081c82b17dd8e069","source":{"kind":"arxiv","id":"1705.11136","version":2},"attestation_state":"computed","paper":{"title":"Representation Learning by Rotating Your Faces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luan Tran, Xiaoming Liu, Xi Yin","submitted_at":"2017-05-31T15:18:37Z","abstract_excerpt":"The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes a Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator enables DR"},"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":"1705.11136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-31T15:18:37Z","cross_cats_sorted":[],"title_canon_sha256":"b90ca6cb0c6af13d9a75fe0c4a29d74addb242b8823f7463653de3aa6dbae022","abstract_canon_sha256":"c4c0e485579da5421aba2480cf5b6c00445ab80fedc0a7bcc6e47954ac15218d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:56.695477Z","signature_b64":"uc21pkUM9Pvz117/mgn+/o65fEfXAAOrhQw/gA5nSFeHmyK2+4bhG3qKTsBjP3VgAYOORFDefKgoPtu57mQ8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb8c842d208870721a4789ecb72ea487f54eceb2a5c503da081c82b17dd8e069","last_reissued_at":"2026-05-18T00:05:56.695000Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:56.695000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Representation Learning by Rotating Your Faces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luan Tran, Xiaoming Liu, Xi Yin","submitted_at":"2017-05-31T15:18:37Z","abstract_excerpt":"The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes a Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator enables DR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.11136","kind":"arxiv","version":2},"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":"1705.11136","created_at":"2026-05-18T00:05:56.695074+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.11136v2","created_at":"2026-05-18T00:05:56.695074+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.11136","created_at":"2026-05-18T00:05:56.695074+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OGIILJARBYH","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OGIILJARBYHEGSH","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OGIILJA","created_at":"2026-05-18T12:31:05.417338+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/7OGIILJARBYHEGSHRHWLOLVEQ7","json":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7.json","graph_json":"https://pith.science/api/pith-number/7OGIILJARBYHEGSHRHWLOLVEQ7/graph.json","events_json":"https://pith.science/api/pith-number/7OGIILJARBYHEGSHRHWLOLVEQ7/events.json","paper":"https://pith.science/paper/7OGIILJA"},"agent_actions":{"view_html":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7","download_json":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7.json","view_paper":"https://pith.science/paper/7OGIILJA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.11136&json=true","fetch_graph":"https://pith.science/api/pith-number/7OGIILJARBYHEGSHRHWLOLVEQ7/graph.json","fetch_events":"https://pith.science/api/pith-number/7OGIILJARBYHEGSHRHWLOLVEQ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7/action/storage_attestation","attest_author":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7/action/author_attestation","sign_citation":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7/action/citation_signature","submit_replication":"https://pith.science/pith/7OGIILJARBYHEGSHRHWLOLVEQ7/action/replication_record"}},"created_at":"2026-05-18T00:05:56.695074+00:00","updated_at":"2026-05-18T00:05:56.695074+00:00"}