{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:D2UBBS7IIUJOFCWY4X7JETEHUQ","short_pith_number":"pith:D2UBBS7I","schema_version":"1.0","canonical_sha256":"1ea810cbe84512e28ad8e5fe924c87a427b2085042e4ad5758bdc08bd9e0b05b","source":{"kind":"arxiv","id":"1907.06312","version":1},"attestation_state":"computed","paper":{"title":"Exploring Deep Anomaly Detection Methods Based on Capsule Net","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Xiaoyan Li, Yifeng Li","submitted_at":"2019-07-15T02:15:58Z","abstract_excerpt":"In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-probability-based and a reconstruction-error-based normality score functions for evaluating the \"outlierness\" of unseen images. Our results on three datasets demonstrate that the prediction-probability-based method performs consistently well, while the reconstruction-error-based appr"},"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":"1907.06312","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-15T02:15:58Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"bb3a8cfb9eff506a5b4f0c3b8ec7610e68719ef873ee91cda546bcb998fecdd9","abstract_canon_sha256":"ce3e30bf9758c87dfdb3095b6a311b681cb63662f22830ee0ee1384766cb76b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:37.957511Z","signature_b64":"XMzYg/mBcLpZi8uoUqt/vcvMplH1e04pLp394RXkG8mKBudUTXGYdDPIwTWJHzZ8gr2k8ihOKMU7m1m3qRaPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ea810cbe84512e28ad8e5fe924c87a427b2085042e4ad5758bdc08bd9e0b05b","last_reissued_at":"2026-05-17T23:40:37.956918Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:37.956918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Deep Anomaly Detection Methods Based on Capsule Net","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Xiaoyan Li, Yifeng Li","submitted_at":"2019-07-15T02:15:58Z","abstract_excerpt":"In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-probability-based and a reconstruction-error-based normality score functions for evaluating the \"outlierness\" of unseen images. Our results on three datasets demonstrate that the prediction-probability-based method performs consistently well, while the reconstruction-error-based appr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06312","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":""},"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":"1907.06312","created_at":"2026-05-17T23:40:37.957013+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.06312v1","created_at":"2026-05-17T23:40:37.957013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06312","created_at":"2026-05-17T23:40:37.957013+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2UBBS7IIUJO","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2UBBS7IIUJOFCWY","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2UBBS7I","created_at":"2026-05-18T12:33:15.570797+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/D2UBBS7IIUJOFCWY4X7JETEHUQ","json":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ.json","graph_json":"https://pith.science/api/pith-number/D2UBBS7IIUJOFCWY4X7JETEHUQ/graph.json","events_json":"https://pith.science/api/pith-number/D2UBBS7IIUJOFCWY4X7JETEHUQ/events.json","paper":"https://pith.science/paper/D2UBBS7I"},"agent_actions":{"view_html":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ","download_json":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ.json","view_paper":"https://pith.science/paper/D2UBBS7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.06312&json=true","fetch_graph":"https://pith.science/api/pith-number/D2UBBS7IIUJOFCWY4X7JETEHUQ/graph.json","fetch_events":"https://pith.science/api/pith-number/D2UBBS7IIUJOFCWY4X7JETEHUQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ/action/storage_attestation","attest_author":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ/action/author_attestation","sign_citation":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ/action/citation_signature","submit_replication":"https://pith.science/pith/D2UBBS7IIUJOFCWY4X7JETEHUQ/action/replication_record"}},"created_at":"2026-05-17T23:40:37.957013+00:00","updated_at":"2026-05-17T23:40:37.957013+00:00"}