{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MUC5MPVQRMB3L5AHNMCEZBMW2X","short_pith_number":"pith:MUC5MPVQ","schema_version":"1.0","canonical_sha256":"6505d63eb08b03b5f4076b044c8596d5e3f3427ef17fd75661926a002229dab4","source":{"kind":"arxiv","id":"2003.02977","version":3},"attestation_state":"computed","paper":{"title":"Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Qing Yan, Yali Amit, Zhisheng Xiao","submitted_at":"2020-03-06T00:30:38Z","abstract_excerpt":"Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, some recent studies show that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we ma"},"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":"2003.02977","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-06T00:30:38Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"428ebbda844a561b6c9371ae1a50496c982e90e95881acc1a13626f4100f6133","abstract_canon_sha256":"680969f9291f99b549412137334763237ce56284bba2f6cd87dbef0e82f124f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:51.231713Z","signature_b64":"OLJbKDV7wAFEhCGNLGesZhwPvCBI1Y+bNa3qglyXomwo7I6If8lOjZXpyP/4zEoCaygbXA/6HSPLZYEsp0yrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6505d63eb08b03b5f4076b044c8596d5e3f3427ef17fd75661926a002229dab4","last_reissued_at":"2026-07-05T01:41:51.231261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:51.231261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Qing Yan, Yali Amit, Zhisheng Xiao","submitted_at":"2020-03-06T00:30:38Z","abstract_excerpt":"Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, some recent studies show that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02977","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/2003.02977/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":"2003.02977","created_at":"2026-07-05T01:41:51.231326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.02977v3","created_at":"2026-07-05T01:41:51.231326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02977","created_at":"2026-07-05T01:41:51.231326+00:00"},{"alias_kind":"pith_short_12","alias_value":"MUC5MPVQRMB3","created_at":"2026-07-05T01:41:51.231326+00:00"},{"alias_kind":"pith_short_16","alias_value":"MUC5MPVQRMB3L5AH","created_at":"2026-07-05T01:41:51.231326+00:00"},{"alias_kind":"pith_short_8","alias_value":"MUC5MPVQ","created_at":"2026-07-05T01:41:51.231326+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/MUC5MPVQRMB3L5AHNMCEZBMW2X","json":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X.json","graph_json":"https://pith.science/api/pith-number/MUC5MPVQRMB3L5AHNMCEZBMW2X/graph.json","events_json":"https://pith.science/api/pith-number/MUC5MPVQRMB3L5AHNMCEZBMW2X/events.json","paper":"https://pith.science/paper/MUC5MPVQ"},"agent_actions":{"view_html":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X","download_json":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X.json","view_paper":"https://pith.science/paper/MUC5MPVQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.02977&json=true","fetch_graph":"https://pith.science/api/pith-number/MUC5MPVQRMB3L5AHNMCEZBMW2X/graph.json","fetch_events":"https://pith.science/api/pith-number/MUC5MPVQRMB3L5AHNMCEZBMW2X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X/action/storage_attestation","attest_author":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X/action/author_attestation","sign_citation":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X/action/citation_signature","submit_replication":"https://pith.science/pith/MUC5MPVQRMB3L5AHNMCEZBMW2X/action/replication_record"}},"created_at":"2026-07-05T01:41:51.231326+00:00","updated_at":"2026-07-05T01:41:51.231326+00:00"}