{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:APXBDITE2V3AUKQKQA5A3LJEKT","short_pith_number":"pith:APXBDITE","schema_version":"1.0","canonical_sha256":"03ee11a264d5760a2a0a803a0dad2454fc6de32d56329466e3ff3080cc536fb6","source":{"kind":"arxiv","id":"2106.05410","version":4},"attestation_state":"computed","paper":{"title":"DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hadi Hojjati, Narges Armanfard","submitted_at":"2021-06-09T21:57:41Z","abstract_excerpt":"Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which "},"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":"2106.05410","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-09T21:57:41Z","cross_cats_sorted":[],"title_canon_sha256":"d5fe7a6bfb31c2961d0fc7c7af9c656a70a868eee4552621c6f4a7013c9906ed","abstract_canon_sha256":"e6af378598247be25481920c019c9eac868211cffcdc50a344a65cb9a25b4499"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:33.626895Z","signature_b64":"w0vwEsGtnhQJRzF744AA0tmi6UzlPKmrCkZ6f39F55vuB2iTE4NJWtmYAenYqHLTXe2itRXtaWX7PYldKAxABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03ee11a264d5760a2a0a803a0dad2454fc6de32d56329466e3ff3080cc536fb6","last_reissued_at":"2026-07-05T07:35:33.626409Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:33.626409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hadi Hojjati, Narges Armanfard","submitted_at":"2021-06-09T21:57:41Z","abstract_excerpt":"Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.05410","kind":"arxiv","version":4},"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/2106.05410/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":"2106.05410","created_at":"2026-07-05T07:35:33.626478+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.05410v4","created_at":"2026-07-05T07:35:33.626478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.05410","created_at":"2026-07-05T07:35:33.626478+00:00"},{"alias_kind":"pith_short_12","alias_value":"APXBDITE2V3A","created_at":"2026-07-05T07:35:33.626478+00:00"},{"alias_kind":"pith_short_16","alias_value":"APXBDITE2V3AUKQK","created_at":"2026-07-05T07:35:33.626478+00:00"},{"alias_kind":"pith_short_8","alias_value":"APXBDITE","created_at":"2026-07-05T07:35:33.626478+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.10792","citing_title":"Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT","json":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT.json","graph_json":"https://pith.science/api/pith-number/APXBDITE2V3AUKQKQA5A3LJEKT/graph.json","events_json":"https://pith.science/api/pith-number/APXBDITE2V3AUKQKQA5A3LJEKT/events.json","paper":"https://pith.science/paper/APXBDITE"},"agent_actions":{"view_html":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT","download_json":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT.json","view_paper":"https://pith.science/paper/APXBDITE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.05410&json=true","fetch_graph":"https://pith.science/api/pith-number/APXBDITE2V3AUKQKQA5A3LJEKT/graph.json","fetch_events":"https://pith.science/api/pith-number/APXBDITE2V3AUKQKQA5A3LJEKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT/action/storage_attestation","attest_author":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT/action/author_attestation","sign_citation":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT/action/citation_signature","submit_replication":"https://pith.science/pith/APXBDITE2V3AUKQKQA5A3LJEKT/action/replication_record"}},"created_at":"2026-07-05T07:35:33.626478+00:00","updated_at":"2026-07-05T07:35:33.626478+00:00"}