{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LZPHMZMSLY44BHKK63YLWLPDUM","short_pith_number":"pith:LZPHMZMS","schema_version":"1.0","canonical_sha256":"5e5e7665925e39c09d4af6f0bb2de3a316e33029e9e3539ae4fed0e3eb1734b1","source":{"kind":"arxiv","id":"2212.12720","version":2},"attestation_state":"computed","paper":{"title":"Boosting Out-of-Distribution Detection with Multiple Pre-trained Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Chuanlong Xie, Falong Tan, Feng Xue, Zhenguo Li, Zi He","submitted_at":"2022-12-24T12:11:38Z","abstract_excerpt":"Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely deploying machine learning systems in the open world. Recently, post hoc detection utilizing pre-trained models has shown promising performance and can be scaled to large-scale problems. This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods? In this work, we propose a detection enhancement method by ensembling multipl"},"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":"2212.12720","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-24T12:11:38Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"4de83e2d9da56efb038c34310072e3ca4a5c0d24bde8f5e748ea04020636f0a1","abstract_canon_sha256":"3b3d79b8658590305bd3741225cf697298c58ee559e4cd0ff01643a3d4331208"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:32:33.730021Z","signature_b64":"/jMyrvVt8kWd6EEOX/pgLN9pGoSrIZSNjGnhB1qmjQgIQ8lSa7pus6KTLuIViord5s8EmLfYHEJtChVQ/CdzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e5e7665925e39c09d4af6f0bb2de3a316e33029e9e3539ae4fed0e3eb1734b1","last_reissued_at":"2026-07-05T05:32:33.729512Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:32:33.729512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boosting Out-of-Distribution Detection with Multiple Pre-trained Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Chuanlong Xie, Falong Tan, Feng Xue, Zhenguo Li, Zi He","submitted_at":"2022-12-24T12:11:38Z","abstract_excerpt":"Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely deploying machine learning systems in the open world. Recently, post hoc detection utilizing pre-trained models has shown promising performance and can be scaled to large-scale problems. This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods? In this work, we propose a detection enhancement method by ensembling multipl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.12720","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/2212.12720/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":"2212.12720","created_at":"2026-07-05T05:32:33.729573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.12720v2","created_at":"2026-07-05T05:32:33.729573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.12720","created_at":"2026-07-05T05:32:33.729573+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZPHMZMSLY44","created_at":"2026-07-05T05:32:33.729573+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZPHMZMSLY44BHKK","created_at":"2026-07-05T05:32:33.729573+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZPHMZMS","created_at":"2026-07-05T05:32:33.729573+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/LZPHMZMSLY44BHKK63YLWLPDUM","json":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM.json","graph_json":"https://pith.science/api/pith-number/LZPHMZMSLY44BHKK63YLWLPDUM/graph.json","events_json":"https://pith.science/api/pith-number/LZPHMZMSLY44BHKK63YLWLPDUM/events.json","paper":"https://pith.science/paper/LZPHMZMS"},"agent_actions":{"view_html":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM","download_json":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM.json","view_paper":"https://pith.science/paper/LZPHMZMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.12720&json=true","fetch_graph":"https://pith.science/api/pith-number/LZPHMZMSLY44BHKK63YLWLPDUM/graph.json","fetch_events":"https://pith.science/api/pith-number/LZPHMZMSLY44BHKK63YLWLPDUM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM/action/storage_attestation","attest_author":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM/action/author_attestation","sign_citation":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM/action/citation_signature","submit_replication":"https://pith.science/pith/LZPHMZMSLY44BHKK63YLWLPDUM/action/replication_record"}},"created_at":"2026-07-05T05:32:33.729573+00:00","updated_at":"2026-07-05T05:32:33.729573+00:00"}