{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:CM23V74P7SNL5PR7QQUXJHQIZA","short_pith_number":"pith:CM23V74P","canonical_record":{"source":{"id":"2107.00191","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-01T03:04:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0d587094558f1434e0105c36ee685effbb4c70f797b4de6b7a12dc4a8fc91c91","abstract_canon_sha256":"03f90b341e43635668070ca43eebb426fe085a2fa19b644f97940e9aa71cfd65"},"schema_version":"1.0"},"canonical_sha256":"1335baff8ffc9abebe3f8429749e08c8384ebd9bd43b080a42f1ee17a70f96f6","source":{"kind":"arxiv","id":"2107.00191","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.00191","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"arxiv_version","alias_value":"2107.00191v1","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.00191","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_12","alias_value":"CM23V74P7SNL","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_16","alias_value":"CM23V74P7SNL5PR7","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_8","alias_value":"CM23V74P","created_at":"2026-07-05T02:54:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:CM23V74P7SNL5PR7QQUXJHQIZA","target":"record","payload":{"canonical_record":{"source":{"id":"2107.00191","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-01T03:04:47Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0d587094558f1434e0105c36ee685effbb4c70f797b4de6b7a12dc4a8fc91c91","abstract_canon_sha256":"03f90b341e43635668070ca43eebb426fe085a2fa19b644f97940e9aa71cfd65"},"schema_version":"1.0"},"canonical_sha256":"1335baff8ffc9abebe3f8429749e08c8384ebd9bd43b080a42f1ee17a70f96f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:21.931956Z","signature_b64":"zAmSjNSymu8uMy08Jl+OO2F9Lo8yme8LflipqdmFR/5lVAbJxqwx/UJnRJjaG68rV6TKJqk2VklSMdTA+mkoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1335baff8ffc9abebe3f8429749e08c8384ebd9bd43b080a42f1ee17a70f96f6","last_reissued_at":"2026-07-05T02:54:21.931609Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:21.931609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.00191","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:54:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xrWmhxHgXC+t8b9JMlC6vkpVL7GAwV+Pk5ttNW8Ra1xHixHNihVqF5ZKnUFTy5XrRXmHV91xCtdpfIs6fveeDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:46:03.936586Z"},"content_sha256":"321ba3148a67c2ef8f7369cfebd6d1e82dd4a5ba18d4a8b733f9266ef2c0304e","schema_version":"1.0","event_id":"sha256:321ba3148a67c2ef8f7369cfebd6d1e82dd4a5ba18d4a8b733f9266ef2c0304e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:CM23V74P7SNL5PR7QQUXJHQIZA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jooeun Kim, Kirill Chechil, Minje Park, Seok-Yong Byun, Wonju Lee","submitted_at":"2021-07-01T03:04:47Z","abstract_excerpt":"While many real-world data streams imply that they change frequently in a nonstationary way, most of deep learning methods optimize neural networks on training data, and this leads to severe performance degradation when dataset shift happens. However, it is less possible to annotate or inspect newly streamed data by humans, and thus it is desired to measure model drift at inference time in an unsupervised manner. In this paper, we propose a novel method of model drift estimation by exploiting statistics of batch normalization layer on unlabeled test data. To remedy possible sampling error of s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.00191","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.00191/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":"2026-07-05T02:54:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f1JrDtq4Ed/DZgkNahedObKpMePkOwkiI1uoJUvbYh6nvzGgebnXpbIEz7uqkmzG6fxP6Miy7t66dHE4EMLDCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:46:03.937128Z"},"content_sha256":"09c070ec9fb07f3ee375744791bafcb2facfad68d29f56bd7fd3d20240192dd6","schema_version":"1.0","event_id":"sha256:09c070ec9fb07f3ee375744791bafcb2facfad68d29f56bd7fd3d20240192dd6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CM23V74P7SNL5PR7QQUXJHQIZA/bundle.json","state_url":"https://pith.science/pith/CM23V74P7SNL5PR7QQUXJHQIZA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CM23V74P7SNL5PR7QQUXJHQIZA/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":"2026-08-06T02:46:03Z","links":{"resolver":"https://pith.science/pith/CM23V74P7SNL5PR7QQUXJHQIZA","bundle":"https://pith.science/pith/CM23V74P7SNL5PR7QQUXJHQIZA/bundle.json","state":"https://pith.science/pith/CM23V74P7SNL5PR7QQUXJHQIZA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CM23V74P7SNL5PR7QQUXJHQIZA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:CM23V74P7SNL5PR7QQUXJHQIZA","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":"03f90b341e43635668070ca43eebb426fe085a2fa19b644f97940e9aa71cfd65","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-01T03:04:47Z","title_canon_sha256":"0d587094558f1434e0105c36ee685effbb4c70f797b4de6b7a12dc4a8fc91c91"},"schema_version":"1.0","source":{"id":"2107.00191","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.00191","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"arxiv_version","alias_value":"2107.00191v1","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.00191","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_12","alias_value":"CM23V74P7SNL","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_16","alias_value":"CM23V74P7SNL5PR7","created_at":"2026-07-05T02:54:21Z"},{"alias_kind":"pith_short_8","alias_value":"CM23V74P","created_at":"2026-07-05T02:54:21Z"}],"graph_snapshots":[{"event_id":"sha256:09c070ec9fb07f3ee375744791bafcb2facfad68d29f56bd7fd3d20240192dd6","target":"graph","created_at":"2026-07-05T02:54:21Z","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/2107.00191/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While many real-world data streams imply that they change frequently in a nonstationary way, most of deep learning methods optimize neural networks on training data, and this leads to severe performance degradation when dataset shift happens. However, it is less possible to annotate or inspect newly streamed data by humans, and thus it is desired to measure model drift at inference time in an unsupervised manner. In this paper, we propose a novel method of model drift estimation by exploiting statistics of batch normalization layer on unlabeled test data. To remedy possible sampling error of s","authors_text":"Jooeun Kim, Kirill Chechil, Minje Park, Seok-Yong Byun, Wonju Lee","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-01T03:04:47Z","title":"Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.00191","kind":"arxiv","version":1},"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":"sha256:321ba3148a67c2ef8f7369cfebd6d1e82dd4a5ba18d4a8b733f9266ef2c0304e","target":"record","created_at":"2026-07-05T02:54:21Z","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":"03f90b341e43635668070ca43eebb426fe085a2fa19b644f97940e9aa71cfd65","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-01T03:04:47Z","title_canon_sha256":"0d587094558f1434e0105c36ee685effbb4c70f797b4de6b7a12dc4a8fc91c91"},"schema_version":"1.0","source":{"id":"2107.00191","kind":"arxiv","version":1}},"canonical_sha256":"1335baff8ffc9abebe3f8429749e08c8384ebd9bd43b080a42f1ee17a70f96f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1335baff8ffc9abebe3f8429749e08c8384ebd9bd43b080a42f1ee17a70f96f6","first_computed_at":"2026-07-05T02:54:21.931609Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:54:21.931609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zAmSjNSymu8uMy08Jl+OO2F9Lo8yme8LflipqdmFR/5lVAbJxqwx/UJnRJjaG68rV6TKJqk2VklSMdTA+mkoCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:54:21.931956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.00191","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:321ba3148a67c2ef8f7369cfebd6d1e82dd4a5ba18d4a8b733f9266ef2c0304e","sha256:09c070ec9fb07f3ee375744791bafcb2facfad68d29f56bd7fd3d20240192dd6"],"state_sha256":"6e01539d7065adbecdaec208394a0222a0188deda0cb025ca4a767df6cbe08f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fK3rBDdVKa4vg7wVOxw/H5IPrrA8fS7WaNy7k2L5vmfkb2COXh1fWpECh/8gN9yNlddRJgIHGsJ2ccoV6O/+Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T02:46:03.940404Z","bundle_sha256":"c0075f0b90cee06aee1fcb97f642760aea6484ebfe9a9d4179b2815394fed67c"}}