{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LCEEHVW7W67GHHHDVI5YV4TVXM","short_pith_number":"pith:LCEEHVW7","schema_version":"1.0","canonical_sha256":"588843d6dfb7be639ce3aa3b8af275bb3b8276d401fa4b2ba0e7ef6cb757bb5b","source":{"kind":"arxiv","id":"1712.00955","version":1},"attestation_state":"computed","paper":{"title":"Composite Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingdong Wang, Ting Zhang","submitted_at":"2017-12-04T08:48:44Z","abstract_excerpt":"This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$) elements, each of which is selected from a different dictionary, to accurately approximate a $D$-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the dis"},"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":"1712.00955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-04T08:48:44Z","cross_cats_sorted":[],"title_canon_sha256":"ddab7acf82516c091756169325351c299b284a2c1d69a2300cdd90186319ec97","abstract_canon_sha256":"d450b7cf4c386b9f2f4d88900da4ae1fb86943d9f97aa35f5668f3b1e3c5a132"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:59.919761Z","signature_b64":"trFyECqq/fZGu7/qYaBBiV7NIHlnfDZ+g/vJAsLDEyWniQ+LBctEPyHk394Llm4XNwJAtluAyrDgLaVFsSDyAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"588843d6dfb7be639ce3aa3b8af275bb3b8276d401fa4b2ba0e7ef6cb757bb5b","last_reissued_at":"2026-05-18T00:28:59.919283Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:59.919283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Composite Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingdong Wang, Ting Zhang","submitted_at":"2017-12-04T08:48:44Z","abstract_excerpt":"This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$) elements, each of which is selected from a different dictionary, to accurately approximate a $D$-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00955","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":"1712.00955","created_at":"2026-05-18T00:28:59.919378+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.00955v1","created_at":"2026-05-18T00:28:59.919378+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00955","created_at":"2026-05-18T00:28:59.919378+00:00"},{"alias_kind":"pith_short_12","alias_value":"LCEEHVW7W67G","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LCEEHVW7W67GHHHD","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LCEEHVW7","created_at":"2026-05-18T12:31:28.150371+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/LCEEHVW7W67GHHHDVI5YV4TVXM","json":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM.json","graph_json":"https://pith.science/api/pith-number/LCEEHVW7W67GHHHDVI5YV4TVXM/graph.json","events_json":"https://pith.science/api/pith-number/LCEEHVW7W67GHHHDVI5YV4TVXM/events.json","paper":"https://pith.science/paper/LCEEHVW7"},"agent_actions":{"view_html":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM","download_json":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM.json","view_paper":"https://pith.science/paper/LCEEHVW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.00955&json=true","fetch_graph":"https://pith.science/api/pith-number/LCEEHVW7W67GHHHDVI5YV4TVXM/graph.json","fetch_events":"https://pith.science/api/pith-number/LCEEHVW7W67GHHHDVI5YV4TVXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM/action/storage_attestation","attest_author":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM/action/author_attestation","sign_citation":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM/action/citation_signature","submit_replication":"https://pith.science/pith/LCEEHVW7W67GHHHDVI5YV4TVXM/action/replication_record"}},"created_at":"2026-05-18T00:28:59.919378+00:00","updated_at":"2026-05-18T00:28:59.919378+00:00"}