{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:46CBN2BLTZ5WFKMKECDMCQMEHI","short_pith_number":"pith:46CBN2BL","schema_version":"1.0","canonical_sha256":"e78416e82b9e7b62a98a2086c141843a2f45a4f92690905d6785feac6ae293a8","source":{"kind":"arxiv","id":"2101.02568","version":2},"attestation_state":"computed","paper":{"title":"HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Xu, Haiyu Zhao, Jiawei Ren, Shuai Yi, Xiao Ma","submitted_at":"2021-01-06T12:03:19Z","abstract_excerpt":"Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems. Learning discriminative features for Re-ID remains a challenge due to the large variations in the image space, e.g., continuously changing human poses, illuminations and point of views. In this paper, we propose HAVANA, a novel extensible, light-weight HierArchical and VAriation-Normalized Autoencoder that learns features robust to intra-class variations. In contrast to existing generative approaches that prune the variations with heavy extra supervised signals, HAVANA suppresses the intra-class var"},"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":"2101.02568","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-01-06T12:03:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4c2f98b66f2c4361855424c860502d256c444e5c5d6e6d8670fcc5ddcda6107f","abstract_canon_sha256":"9ca7f4defc22a8ee2827241c3aba5f1057d3e7099f326c3eb8ada6d29bfb03fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:05:46.668205Z","signature_b64":"SmRwt+PMXjr4F7Y7eKn4zydIQta3KY/IG/K+C10Ni4lpJQHU2993CofgfO/quNvLbykQGu3Kz805xYwbullQAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e78416e82b9e7b62a98a2086c141843a2f45a4f92690905d6785feac6ae293a8","last_reissued_at":"2026-07-05T02:05:46.667859Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:05:46.667859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chen Xu, Haiyu Zhao, Jiawei Ren, Shuai Yi, Xiao Ma","submitted_at":"2021-01-06T12:03:19Z","abstract_excerpt":"Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems. Learning discriminative features for Re-ID remains a challenge due to the large variations in the image space, e.g., continuously changing human poses, illuminations and point of views. In this paper, we propose HAVANA, a novel extensible, light-weight HierArchical and VAriation-Normalized Autoencoder that learns features robust to intra-class variations. In contrast to existing generative approaches that prune the variations with heavy extra supervised signals, HAVANA suppresses the intra-class var"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.02568","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.02568/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":"2101.02568","created_at":"2026-07-05T02:05:46.667914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.02568v2","created_at":"2026-07-05T02:05:46.667914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.02568","created_at":"2026-07-05T02:05:46.667914+00:00"},{"alias_kind":"pith_short_12","alias_value":"46CBN2BLTZ5W","created_at":"2026-07-05T02:05:46.667914+00:00"},{"alias_kind":"pith_short_16","alias_value":"46CBN2BLTZ5WFKMK","created_at":"2026-07-05T02:05:46.667914+00:00"},{"alias_kind":"pith_short_8","alias_value":"46CBN2BL","created_at":"2026-07-05T02:05:46.667914+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/46CBN2BLTZ5WFKMKECDMCQMEHI","json":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI.json","graph_json":"https://pith.science/api/pith-number/46CBN2BLTZ5WFKMKECDMCQMEHI/graph.json","events_json":"https://pith.science/api/pith-number/46CBN2BLTZ5WFKMKECDMCQMEHI/events.json","paper":"https://pith.science/paper/46CBN2BL"},"agent_actions":{"view_html":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI","download_json":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI.json","view_paper":"https://pith.science/paper/46CBN2BL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.02568&json=true","fetch_graph":"https://pith.science/api/pith-number/46CBN2BLTZ5WFKMKECDMCQMEHI/graph.json","fetch_events":"https://pith.science/api/pith-number/46CBN2BLTZ5WFKMKECDMCQMEHI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI/action/storage_attestation","attest_author":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI/action/author_attestation","sign_citation":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI/action/citation_signature","submit_replication":"https://pith.science/pith/46CBN2BLTZ5WFKMKECDMCQMEHI/action/replication_record"}},"created_at":"2026-07-05T02:05:46.667914+00:00","updated_at":"2026-07-05T02:05:46.667914+00:00"}