{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:J4ZEFRVXKWC3IGC6M33VTP2HKH","short_pith_number":"pith:J4ZEFRVX","schema_version":"1.0","canonical_sha256":"4f3242c6b75585b4185e66f759bf4751ff45a96e65a68f5b150f97623effdb83","source":{"kind":"arxiv","id":"2206.11723","version":8},"attestation_state":"computed","paper":{"title":"Self-Supervised Training with Autoencoders for Visual Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander Bauer, Klaus-Robert M\\\"uller, Shinichi Nakajima","submitted_at":"2022-06-23T14:16:30Z","abstract_excerpt":"We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. Here, regularized autoencoders provide a popular approach by learning the identity mapping on the set of normal examples, while trying to prevent good reconstruction on points outside of the manifold. Typically, this goal is implemented by controlling the capacity of the model, either directly by reducing the size of the bottleneck layer or implicitly by imposing some sparsity (or contraction) constraints on parts of the corresponding network. However, nei"},"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":"2206.11723","kind":"arxiv","version":8},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-23T14:16:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cd19d62cbb35ea3560a8e3defecc75dc773eca78c5bdb1a421bfc9b6de0bd085","abstract_canon_sha256":"5fc0da0399487a78571f63c1a4ee8777d6da5a53b753fce4d3b36e0d9f74277a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:54.433462Z","signature_b64":"poV8NXm28bfhdk8dMfi4Kz9z8/6HYPydjF+csIpJ+rpkLoGlakliqv6NzdzQ2FnfcsVgYpI7m9W1yU8tq1cZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f3242c6b75585b4185e66f759bf4751ff45a96e65a68f5b150f97623effdb83","last_reissued_at":"2026-07-05T08:17:54.433091Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:54.433091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Training with Autoencoders for Visual Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Alexander Bauer, Klaus-Robert M\\\"uller, Shinichi Nakajima","submitted_at":"2022-06-23T14:16:30Z","abstract_excerpt":"We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. Here, regularized autoencoders provide a popular approach by learning the identity mapping on the set of normal examples, while trying to prevent good reconstruction on points outside of the manifold. Typically, this goal is implemented by controlling the capacity of the model, either directly by reducing the size of the bottleneck layer or implicitly by imposing some sparsity (or contraction) constraints on parts of the corresponding network. However, nei"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.11723","kind":"arxiv","version":8},"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/2206.11723/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":"2206.11723","created_at":"2026-07-05T08:17:54.433153+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.11723v8","created_at":"2026-07-05T08:17:54.433153+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.11723","created_at":"2026-07-05T08:17:54.433153+00:00"},{"alias_kind":"pith_short_12","alias_value":"J4ZEFRVXKWC3","created_at":"2026-07-05T08:17:54.433153+00:00"},{"alias_kind":"pith_short_16","alias_value":"J4ZEFRVXKWC3IGC6","created_at":"2026-07-05T08:17:54.433153+00:00"},{"alias_kind":"pith_short_8","alias_value":"J4ZEFRVX","created_at":"2026-07-05T08:17:54.433153+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/J4ZEFRVXKWC3IGC6M33VTP2HKH","json":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH.json","graph_json":"https://pith.science/api/pith-number/J4ZEFRVXKWC3IGC6M33VTP2HKH/graph.json","events_json":"https://pith.science/api/pith-number/J4ZEFRVXKWC3IGC6M33VTP2HKH/events.json","paper":"https://pith.science/paper/J4ZEFRVX"},"agent_actions":{"view_html":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH","download_json":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH.json","view_paper":"https://pith.science/paper/J4ZEFRVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.11723&json=true","fetch_graph":"https://pith.science/api/pith-number/J4ZEFRVXKWC3IGC6M33VTP2HKH/graph.json","fetch_events":"https://pith.science/api/pith-number/J4ZEFRVXKWC3IGC6M33VTP2HKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH/action/storage_attestation","attest_author":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH/action/author_attestation","sign_citation":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH/action/citation_signature","submit_replication":"https://pith.science/pith/J4ZEFRVXKWC3IGC6M33VTP2HKH/action/replication_record"}},"created_at":"2026-07-05T08:17:54.433153+00:00","updated_at":"2026-07-05T08:17:54.433153+00:00"}