{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:AKXUYDOWMJAEQEGBPA4KS2HSJD","short_pith_number":"pith:AKXUYDOW","canonical_record":{"source":{"id":"1812.00442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T18:31:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4b392607b8964d11a94c3c55d9d6f60120263f44b09037c951f74a25351ad2bf","abstract_canon_sha256":"1d05b6c0236a88230a9fe993899afae6394595e17591999769460ad9ad68b06b"},"schema_version":"1.0"},"canonical_sha256":"02af4c0dd662404810c17838a968f248c21e03c169db6e7c594f2b86ecc94fd9","source":{"kind":"arxiv","id":"1812.00442","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.00442","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"arxiv_version","alias_value":"1812.00442v1","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00442","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"pith_short_12","alias_value":"AKXUYDOWMJAE","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AKXUYDOWMJAEQEGB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AKXUYDOW","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:AKXUYDOWMJAEQEGBPA4KS2HSJD","target":"record","payload":{"canonical_record":{"source":{"id":"1812.00442","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T18:31:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4b392607b8964d11a94c3c55d9d6f60120263f44b09037c951f74a25351ad2bf","abstract_canon_sha256":"1d05b6c0236a88230a9fe993899afae6394595e17591999769460ad9ad68b06b"},"schema_version":"1.0"},"canonical_sha256":"02af4c0dd662404810c17838a968f248c21e03c169db6e7c594f2b86ecc94fd9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:21.320847Z","signature_b64":"ZKNmF6YT7qdoyecZrZq5lbbhONQ8pKDcULX90uhgbXM62Ffqlbth9CAxRd8TnBDHvRMHJyuUy9YP/ZnslMW2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02af4c0dd662404810c17838a968f248c21e03c169db6e7c594f2b86ecc94fd9","last_reissued_at":"2026-05-17T23:59:21.320339Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:21.320339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.00442","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2UWR1Gkwkbqeml2tsQ7Rz/EBg10BuUiA4xxxylI/T94tJdS9O++uOOVmiuU/7I9Ggz5ZNStr7kPMpJWCx/ywAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T02:08:21.231451Z"},"content_sha256":"474ab550f3de56a30e318a43269739ee8d2d0cc90a7dbbf58bf48e03faaa8cb3","schema_version":"1.0","event_id":"sha256:474ab550f3de56a30e318a43269739ee8d2d0cc90a7dbbf58bf48e03faaa8cb3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:AKXUYDOWMJAEQEGBPA4KS2HSJD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Cosine Metric Learning for Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alex Bewley, Nicolai Wojke","submitted_at":"2018-12-02T18:31:45Z","abstract_excerpt":"Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00442","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GXy6H/he+X8fa2W3xeXvwNFKgSNZaH3DkNjA96w/UW+Dwmm6Vo1iJAu+5r8GJbjqgu5HJpIxVztRX6euGaLEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T02:08:21.232027Z"},"content_sha256":"f3d0b1586fd66b171e494308809146b53a31e9c71e88c54b81da2f2fb44eb97a","schema_version":"1.0","event_id":"sha256:f3d0b1586fd66b171e494308809146b53a31e9c71e88c54b81da2f2fb44eb97a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/bundle.json","state_url":"https://pith.science/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T02:08:21Z","links":{"resolver":"https://pith.science/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD","bundle":"https://pith.science/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/bundle.json","state":"https://pith.science/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKXUYDOWMJAEQEGBPA4KS2HSJD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AKXUYDOWMJAEQEGBPA4KS2HSJD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1d05b6c0236a88230a9fe993899afae6394595e17591999769460ad9ad68b06b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T18:31:45Z","title_canon_sha256":"4b392607b8964d11a94c3c55d9d6f60120263f44b09037c951f74a25351ad2bf"},"schema_version":"1.0","source":{"id":"1812.00442","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.00442","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"arxiv_version","alias_value":"1812.00442v1","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00442","created_at":"2026-05-17T23:59:21Z"},{"alias_kind":"pith_short_12","alias_value":"AKXUYDOWMJAE","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"AKXUYDOWMJAEQEGB","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"AKXUYDOW","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:f3d0b1586fd66b171e494308809146b53a31e9c71e88c54b81da2f2fb44eb97a","target":"graph","created_at":"2026-05-17T23:59:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to","authors_text":"Alex Bewley, Nicolai Wojke","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T18:31:45Z","title":"Deep Cosine Metric Learning for Person Re-Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00442","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:474ab550f3de56a30e318a43269739ee8d2d0cc90a7dbbf58bf48e03faaa8cb3","target":"record","created_at":"2026-05-17T23:59:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1d05b6c0236a88230a9fe993899afae6394595e17591999769460ad9ad68b06b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T18:31:45Z","title_canon_sha256":"4b392607b8964d11a94c3c55d9d6f60120263f44b09037c951f74a25351ad2bf"},"schema_version":"1.0","source":{"id":"1812.00442","kind":"arxiv","version":1}},"canonical_sha256":"02af4c0dd662404810c17838a968f248c21e03c169db6e7c594f2b86ecc94fd9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02af4c0dd662404810c17838a968f248c21e03c169db6e7c594f2b86ecc94fd9","first_computed_at":"2026-05-17T23:59:21.320339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:21.320339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZKNmF6YT7qdoyecZrZq5lbbhONQ8pKDcULX90uhgbXM62Ffqlbth9CAxRd8TnBDHvRMHJyuUy9YP/ZnslMW2CQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:21.320847Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.00442","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:474ab550f3de56a30e318a43269739ee8d2d0cc90a7dbbf58bf48e03faaa8cb3","sha256:f3d0b1586fd66b171e494308809146b53a31e9c71e88c54b81da2f2fb44eb97a"],"state_sha256":"f9fbca04c820147b01dbd538fca536b6e4b514ff64ccca60f3f286e544da8ae5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IhnT/rvNw0JMxUf9LA/ccIOudgWWAUv/bZ8JbLps5os4pKZf0NLGoD2pbtql20cChpzocm3xyeiP6yMFoyrxAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T02:08:21.235710Z","bundle_sha256":"8e84de2759ba44b7023723b61c5e8fc189db70b749a4032938717f16ae2677ef"}}