{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TNOZFOSTZETSD4SPRZ7OBW3DJP","short_pith_number":"pith:TNOZFOST","schema_version":"1.0","canonical_sha256":"9b5d92ba53c92721f24f8e7ee0db634bc3870ab50593110e5b1e2703494c30dc","source":{"kind":"arxiv","id":"2108.04907","version":1},"attestation_state":"computed","paper":{"title":"Flow-based SVDD for anomaly detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jacek Tabor, {\\L}ukasz Maziarka, {\\L}ukasz Struski, Marcin Sendera, Marek \\'Smieja, Przemys{\\l}aw Spurek","submitted_at":"2021-08-10T20:33:15Z","abstract_excerpt":"We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersphere into a single point. Experiments show that FlowSVDD achieves comparable results to the current state-of-the-art methods and significantly outperforms related deep SVDD methods on benchmark datasets."},"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":"2108.04907","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-10T20:33:15Z","cross_cats_sorted":[],"title_canon_sha256":"f3791436bde50cb975e0eb63044d65987e95b6ecce8c734140dac256f677b15f","abstract_canon_sha256":"7cbb36abe3c106a68e896c82b7fbe90493f9edafc472e1f21f03bd78ae962a25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:15.615898Z","signature_b64":"eC2+GIQnBIfITbmuEtifqSHv9VQ+ehAy+b3akxfHQZyoBNn5IxjJeXD1XQrZbj8UAA8I/9Bfp/K+y1BZW+tlAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b5d92ba53c92721f24f8e7ee0db634bc3870ab50593110e5b1e2703494c30dc","last_reissued_at":"2026-07-05T03:07:15.615366Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:15.615366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Flow-based SVDD for anomaly detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jacek Tabor, {\\L}ukasz Maziarka, {\\L}ukasz Struski, Marcin Sendera, Marek \\'Smieja, Przemys{\\l}aw Spurek","submitted_at":"2021-08-10T20:33:15Z","abstract_excerpt":"We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersphere into a single point. Experiments show that FlowSVDD achieves comparable results to the current state-of-the-art methods and significantly outperforms related deep SVDD methods on benchmark datasets."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.04907","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/2108.04907/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":"2108.04907","created_at":"2026-07-05T03:07:15.615430+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.04907v1","created_at":"2026-07-05T03:07:15.615430+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.04907","created_at":"2026-07-05T03:07:15.615430+00:00"},{"alias_kind":"pith_short_12","alias_value":"TNOZFOSTZETS","created_at":"2026-07-05T03:07:15.615430+00:00"},{"alias_kind":"pith_short_16","alias_value":"TNOZFOSTZETSD4SP","created_at":"2026-07-05T03:07:15.615430+00:00"},{"alias_kind":"pith_short_8","alias_value":"TNOZFOST","created_at":"2026-07-05T03:07:15.615430+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/TNOZFOSTZETSD4SPRZ7OBW3DJP","json":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP.json","graph_json":"https://pith.science/api/pith-number/TNOZFOSTZETSD4SPRZ7OBW3DJP/graph.json","events_json":"https://pith.science/api/pith-number/TNOZFOSTZETSD4SPRZ7OBW3DJP/events.json","paper":"https://pith.science/paper/TNOZFOST"},"agent_actions":{"view_html":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP","download_json":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP.json","view_paper":"https://pith.science/paper/TNOZFOST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.04907&json=true","fetch_graph":"https://pith.science/api/pith-number/TNOZFOSTZETSD4SPRZ7OBW3DJP/graph.json","fetch_events":"https://pith.science/api/pith-number/TNOZFOSTZETSD4SPRZ7OBW3DJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP/action/storage_attestation","attest_author":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP/action/author_attestation","sign_citation":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP/action/citation_signature","submit_replication":"https://pith.science/pith/TNOZFOSTZETSD4SPRZ7OBW3DJP/action/replication_record"}},"created_at":"2026-07-05T03:07:15.615430+00:00","updated_at":"2026-07-05T03:07:15.615430+00:00"}