{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ZCRHKJYM3JCYS5FFUHLEPPVN2U","short_pith_number":"pith:ZCRHKJYM","canonical_record":{"source":{"id":"2107.06908","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T18:00:11Z","cross_cats_sorted":[],"title_canon_sha256":"1e488171b066f53ba930f1012974b175d2345575da08e72ba164db7c03158f9b","abstract_canon_sha256":"35f6107e786ab9801c3a190168dda52accbe9f93775401ec06ddf0cd19baf627"},"schema_version":"1.0"},"canonical_sha256":"c8a275270cda458974a5a1d647beadd50e0cd46fda4e1cc98bacf2cfb6ae8bc9","source":{"kind":"arxiv","id":"2107.06908","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06908","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06908v2","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06908","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_12","alias_value":"ZCRHKJYM3JCY","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_16","alias_value":"ZCRHKJYM3JCYS5FF","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_8","alias_value":"ZCRHKJYM","created_at":"2026-07-05T02:58:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ZCRHKJYM3JCYS5FFUHLEPPVN2U","target":"record","payload":{"canonical_record":{"source":{"id":"2107.06908","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T18:00:11Z","cross_cats_sorted":[],"title_canon_sha256":"1e488171b066f53ba930f1012974b175d2345575da08e72ba164db7c03158f9b","abstract_canon_sha256":"35f6107e786ab9801c3a190168dda52accbe9f93775401ec06ddf0cd19baf627"},"schema_version":"1.0"},"canonical_sha256":"c8a275270cda458974a5a1d647beadd50e0cd46fda4e1cc98bacf2cfb6ae8bc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:58:44.134832Z","signature_b64":"eXkdPxT5G0kXLfg2GSDmkVisizviMQYjcLthxa3xJSboEFmoGSMQ8M+68CKWWKjmSsYf7SwuCO8s21xst6kxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8a275270cda458974a5a1d647beadd50e0cd46fda4e1cc98bacf2cfb6ae8bc9","last_reissued_at":"2026-07-05T02:58:44.134490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:58:44.134490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.06908","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:58:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/sQnLfuVS31GD4owW34dl8pf9UaaXVczMr74/axOFXE7IxenHQXU6DQ1N4srU5afVGQ12u66QLCNMVwxSURZAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T08:41:03.593016Z"},"content_sha256":"20ffd900f7f8880613a06630fd8f0eb69d9445a629f067f152292f16af3bb34b","schema_version":"1.0","event_id":"sha256:20ffd900f7f8880613a06630fd8f0eb69d9445a629f067f152292f16af3bb34b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ZCRHKJYM3JCYS5FFUHLEPPVN2U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Understanding Failures in Out-of-Distribution Detection with Deep Generative Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Lily H. Zhang, Mark Goldstein, Rajesh Ranganath","submitted_at":"2021-07-14T18:00:11Z","abstract_excerpt":"Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution. In this work, we explain why this behavior should be attributed to model misestimation. We first prove that no method can guarantee performance beyond random chance without assumptions on which out-distributions are relevant. We then interrogate the typical set hypothesis, the claim that relevant out-distributions can lie in high likelihood regions of the data distri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06908","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/2107.06908/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:58:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hgdcMhoCQNK/AVN+ivsWX2i5o4iyqYpqMG58A5mFd1+iUDMa0V0Dgnt289S0sPmrk2t3q3XRqnZfSMUpNuR0BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T08:41:03.593906Z"},"content_sha256":"7c2adfd763f52294b2535d711631e2d8a13c88b777aa8705875256fdb31f08c2","schema_version":"1.0","event_id":"sha256:7c2adfd763f52294b2535d711631e2d8a13c88b777aa8705875256fdb31f08c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/bundle.json","state_url":"https://pith.science/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/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":"2026-08-10T08:41:03Z","links":{"resolver":"https://pith.science/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U","bundle":"https://pith.science/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/bundle.json","state":"https://pith.science/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZCRHKJYM3JCYS5FFUHLEPPVN2U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ZCRHKJYM3JCYS5FFUHLEPPVN2U","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":"35f6107e786ab9801c3a190168dda52accbe9f93775401ec06ddf0cd19baf627","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T18:00:11Z","title_canon_sha256":"1e488171b066f53ba930f1012974b175d2345575da08e72ba164db7c03158f9b"},"schema_version":"1.0","source":{"id":"2107.06908","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.06908","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"arxiv_version","alias_value":"2107.06908v2","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.06908","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_12","alias_value":"ZCRHKJYM3JCY","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_16","alias_value":"ZCRHKJYM3JCYS5FF","created_at":"2026-07-05T02:58:44Z"},{"alias_kind":"pith_short_8","alias_value":"ZCRHKJYM","created_at":"2026-07-05T02:58:44Z"}],"graph_snapshots":[{"event_id":"sha256:7c2adfd763f52294b2535d711631e2d8a13c88b777aa8705875256fdb31f08c2","target":"graph","created_at":"2026-07-05T02:58:44Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.06908/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution. In this work, we explain why this behavior should be attributed to model misestimation. We first prove that no method can guarantee performance beyond random chance without assumptions on which out-distributions are relevant. We then interrogate the typical set hypothesis, the claim that relevant out-distributions can lie in high likelihood regions of the data distri","authors_text":"Lily H. Zhang, Mark Goldstein, Rajesh Ranganath","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T18:00:11Z","title":"Understanding Failures in Out-of-Distribution Detection with Deep Generative Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.06908","kind":"arxiv","version":2},"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":"sha256:20ffd900f7f8880613a06630fd8f0eb69d9445a629f067f152292f16af3bb34b","target":"record","created_at":"2026-07-05T02:58:44Z","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":"35f6107e786ab9801c3a190168dda52accbe9f93775401ec06ddf0cd19baf627","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-14T18:00:11Z","title_canon_sha256":"1e488171b066f53ba930f1012974b175d2345575da08e72ba164db7c03158f9b"},"schema_version":"1.0","source":{"id":"2107.06908","kind":"arxiv","version":2}},"canonical_sha256":"c8a275270cda458974a5a1d647beadd50e0cd46fda4e1cc98bacf2cfb6ae8bc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c8a275270cda458974a5a1d647beadd50e0cd46fda4e1cc98bacf2cfb6ae8bc9","first_computed_at":"2026-07-05T02:58:44.134490Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:58:44.134490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eXkdPxT5G0kXLfg2GSDmkVisizviMQYjcLthxa3xJSboEFmoGSMQ8M+68CKWWKjmSsYf7SwuCO8s21xst6kxBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:58:44.134832Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.06908","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20ffd900f7f8880613a06630fd8f0eb69d9445a629f067f152292f16af3bb34b","sha256:7c2adfd763f52294b2535d711631e2d8a13c88b777aa8705875256fdb31f08c2"],"state_sha256":"e4efab99a1749048084bd5f0e519e5e6f52b633811117fd09f3c364a838b0e3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iyMV/+xyeCRwaFObANpX/SpnXXZhiuT4qSC41psh2F2uwEBY95hiRW3xq/72HD9fg9QF6LCg3UOBUJUY5rmNCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T08:41:03.599875Z","bundle_sha256":"9510ce24a2a8e9dbd98838134ea60071efff9bcde7faaafcf25a543e42117a7c"}}