{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:U2S2ORHHIA3HSWDWWAOTSZIZSK","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":"5729f07b312b3252b0f61d269b415ef8fff8ff14b5ee15cc4b29f9a348e5c40a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T09:13:20Z","title_canon_sha256":"754df8e45bcfd46ba9de62b63185f807c387ce2962bf50d1ccc09c391a665d23"},"schema_version":"1.0","source":{"id":"2303.17505","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.17505","created_at":"2026-07-05T05:56:35Z"},{"alias_kind":"arxiv_version","alias_value":"2303.17505v1","created_at":"2026-07-05T05:56:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.17505","created_at":"2026-07-05T05:56:35Z"},{"alias_kind":"pith_short_12","alias_value":"U2S2ORHHIA3H","created_at":"2026-07-05T05:56:35Z"},{"alias_kind":"pith_short_16","alias_value":"U2S2ORHHIA3HSWDW","created_at":"2026-07-05T05:56:35Z"},{"alias_kind":"pith_short_8","alias_value":"U2S2ORHH","created_at":"2026-07-05T05:56:35Z"}],"graph_snapshots":[{"event_id":"sha256:7fc662823df0e2a4fe6f09bd2bd0851077f031127ef9e1efa2342aeaf94b2dbb","target":"graph","created_at":"2026-07-05T05:56:35Z","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/2303.17505/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in disease diagnosis. Recently, deep auto-encoder with its variants has demonstrated its advantages in many UAD scenarios. Training on the normal data, these models are expected to locate anomalies by producing higher reconstruction error for the abnormal areas than the normal ones. However, this assumption does not always hold because of the uncontrollable generalization capability. To solve this problem, we present LSGS, ","authors_text":"Bin Chen, Chengxiao Luo, Jiawei Li, Mingqing Wang, Shu-Tao Xia, Zhenyang Li, Zhi Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T09:13:20Z","title":"Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.17505","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":"sha256:30e0d16985232b66650a2ed3d714340d4bfeb78f9c0cf5e15b7b6c4526aca959","target":"record","created_at":"2026-07-05T05:56:35Z","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":"5729f07b312b3252b0f61d269b415ef8fff8ff14b5ee15cc4b29f9a348e5c40a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-29T09:13:20Z","title_canon_sha256":"754df8e45bcfd46ba9de62b63185f807c387ce2962bf50d1ccc09c391a665d23"},"schema_version":"1.0","source":{"id":"2303.17505","kind":"arxiv","version":1}},"canonical_sha256":"a6a5a744e74036795876b01d396519928f5e1def9c5db09ea1fbc0c7209d9411","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6a5a744e74036795876b01d396519928f5e1def9c5db09ea1fbc0c7209d9411","first_computed_at":"2026-07-05T05:56:35.100422Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:56:35.100422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YhofyokYJ4cWHFWJ7cOMW2b5m8L2PFtkDcVbCUxgq8PJZq5XjkIpMhKa1HzobL6SRiJjCg2ISkAWjks1s8R1Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:56:35.100962Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.17505","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:30e0d16985232b66650a2ed3d714340d4bfeb78f9c0cf5e15b7b6c4526aca959","sha256:7fc662823df0e2a4fe6f09bd2bd0851077f031127ef9e1efa2342aeaf94b2dbb"],"state_sha256":"b5af6515aa6c46be8a69018f2d1837c6126ad228efc2ce0e8be7cc6c4ddfefb2"}