{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SNEQXIQFPGZSRMIGTORQKJJCZM","short_pith_number":"pith:SNEQXIQF","schema_version":"1.0","canonical_sha256":"93490ba20579b328b1069ba3052522cb01dbd2235933b67c88c8caa8e4b0aa9c","source":{"kind":"arxiv","id":"2310.03388","version":3},"attestation_state":"computed","paper":{"title":"OpenPatch: a 3D patchwork for Out-Of-Distribution detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Antonio Alliegro, Francesco Cappio Borlino, Paolo Rabino, Tatiana Tommasi","submitted_at":"2023-10-05T08:49:51Z","abstract_excerpt":"Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further computational training effort at every new task. Out-of-distribution detection has attracted significant attention in the last years, however the majority of the studies deal with 2D images ignoring the inherent 3D nature of the real-world and "},"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":"2310.03388","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-05T08:49:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d66cc9ade318bee16ca3122cd36fe1d849af40db4438ce77f1cddad83f5f4d4e","abstract_canon_sha256":"502a1d9a29c5a4223cd53ae83b2125dbb54cd477c0cf0f74833746129fe75cbb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:21.080253Z","signature_b64":"N7CDKMXkkI291v2OMgKh2BJgBIkTjYT5xCgQix7q30m/3uz7RQNu+PLi/+6noKlR1CMXmPJtUvr1f4BXF8HSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93490ba20579b328b1069ba3052522cb01dbd2235933b67c88c8caa8e4b0aa9c","last_reissued_at":"2026-07-05T07:04:21.079896Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:21.079896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenPatch: a 3D patchwork for Out-Of-Distribution detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Antonio Alliegro, Francesco Cappio Borlino, Paolo Rabino, Tatiana Tommasi","submitted_at":"2023-10-05T08:49:51Z","abstract_excerpt":"Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further computational training effort at every new task. Out-of-distribution detection has attracted significant attention in the last years, however the majority of the studies deal with 2D images ignoring the inherent 3D nature of the real-world and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03388","kind":"arxiv","version":3},"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/2310.03388/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":"2310.03388","created_at":"2026-07-05T07:04:21.079954+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.03388v3","created_at":"2026-07-05T07:04:21.079954+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03388","created_at":"2026-07-05T07:04:21.079954+00:00"},{"alias_kind":"pith_short_12","alias_value":"SNEQXIQFPGZS","created_at":"2026-07-05T07:04:21.079954+00:00"},{"alias_kind":"pith_short_16","alias_value":"SNEQXIQFPGZSRMIG","created_at":"2026-07-05T07:04:21.079954+00:00"},{"alias_kind":"pith_short_8","alias_value":"SNEQXIQF","created_at":"2026-07-05T07:04:21.079954+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21892","citing_title":"SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM","json":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM.json","graph_json":"https://pith.science/api/pith-number/SNEQXIQFPGZSRMIGTORQKJJCZM/graph.json","events_json":"https://pith.science/api/pith-number/SNEQXIQFPGZSRMIGTORQKJJCZM/events.json","paper":"https://pith.science/paper/SNEQXIQF"},"agent_actions":{"view_html":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM","download_json":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM.json","view_paper":"https://pith.science/paper/SNEQXIQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.03388&json=true","fetch_graph":"https://pith.science/api/pith-number/SNEQXIQFPGZSRMIGTORQKJJCZM/graph.json","fetch_events":"https://pith.science/api/pith-number/SNEQXIQFPGZSRMIGTORQKJJCZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM/action/storage_attestation","attest_author":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM/action/author_attestation","sign_citation":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM/action/citation_signature","submit_replication":"https://pith.science/pith/SNEQXIQFPGZSRMIGTORQKJJCZM/action/replication_record"}},"created_at":"2026-07-05T07:04:21.079954+00:00","updated_at":"2026-07-05T07:04:21.079954+00:00"}