{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QX5GPKXGUS5IK4SMMT5RERR5N7","short_pith_number":"pith:QX5GPKXG","canonical_record":{"source":{"id":"1901.03407","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T21:36:57Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"11143d0e7cf9ba8fab8c0e5738735047997b7da6285d4f5575edbb14102e1a09","abstract_canon_sha256":"9cb730c9f9e1c5d37c9beb83c99ef6430c7da02a8fff5fced322e60af31aab5b"},"schema_version":"1.0"},"canonical_sha256":"85fa67aae6a4ba85724c64fb12463d6ff1b1e9a53483155f16f9d125cec15ba5","source":{"kind":"arxiv","id":"1901.03407","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03407","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03407v2","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03407","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"pith_short_12","alias_value":"QX5GPKXGUS5I","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QX5GPKXGUS5IK4SM","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QX5GPKXG","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QX5GPKXGUS5IK4SMMT5RERR5N7","target":"record","payload":{"canonical_record":{"source":{"id":"1901.03407","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T21:36:57Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"11143d0e7cf9ba8fab8c0e5738735047997b7da6285d4f5575edbb14102e1a09","abstract_canon_sha256":"9cb730c9f9e1c5d37c9beb83c99ef6430c7da02a8fff5fced322e60af31aab5b"},"schema_version":"1.0"},"canonical_sha256":"85fa67aae6a4ba85724c64fb12463d6ff1b1e9a53483155f16f9d125cec15ba5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:42.263419Z","signature_b64":"RjDEOnRBCnN7qzakfd0YR2+tIhgpMqjK9uGCI8NM5BbgASnvxrt9Pgms7UxlPfU8Ju7I4+BbvFDPO+EzFoe1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85fa67aae6a4ba85724c64fb12463d6ff1b1e9a53483155f16f9d125cec15ba5","last_reissued_at":"2026-05-17T23:55:42.262808Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:42.262808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.03407","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:55:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yBVQNN5ABCjLrp79GwO2qXWYl+3sr1tPDxYOEGxnyAbke11W+wzF29ErEGX7QO6hMxmOyadH22XQvsVvcGPZBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T07:34:54.135385Z"},"content_sha256":"d7c0fc0c951d14b32b4078009d2388f971d1965e9c0e69816365567befb3834b","schema_version":"1.0","event_id":"sha256:d7c0fc0c951d14b32b4078009d2388f971d1965e9c0e69816365567befb3834b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QX5GPKXGUS5IK4SMMT5RERR5N7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning for Anomaly Detection: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Capital Markets Cooperative Research Centre (CMCRC)), HBKU), Raghavendra Chalapathy (University of Sydney, Sanjay Chawla (Qatar Computing Research Institute (QCRI)","submitted_at":"2019-01-10T21:36:57Z","abstract_excerpt":"Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03407","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:55:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cMqODsRi/2QOqTLBnPDVgrNx+3zLwaBKvPipVIQV5XU9UDKhwsuDnohdRnqu0HjQZNHT+kxnKo4VvcsmawyUAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T07:34:54.135776Z"},"content_sha256":"6a68f437bf9ac7ea141490a8a6ecf01dfe12311f6bdc62f0e04ca23843230958","schema_version":"1.0","event_id":"sha256:6a68f437bf9ac7ea141490a8a6ecf01dfe12311f6bdc62f0e04ca23843230958"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/bundle.json","state_url":"https://pith.science/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-20T07:34:54Z","links":{"resolver":"https://pith.science/pith/QX5GPKXGUS5IK4SMMT5RERR5N7","bundle":"https://pith.science/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/bundle.json","state":"https://pith.science/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QX5GPKXGUS5IK4SMMT5RERR5N7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QX5GPKXGUS5IK4SMMT5RERR5N7","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":"9cb730c9f9e1c5d37c9beb83c99ef6430c7da02a8fff5fced322e60af31aab5b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T21:36:57Z","title_canon_sha256":"11143d0e7cf9ba8fab8c0e5738735047997b7da6285d4f5575edbb14102e1a09"},"schema_version":"1.0","source":{"id":"1901.03407","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.03407","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"arxiv_version","alias_value":"1901.03407v2","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03407","created_at":"2026-05-17T23:55:42Z"},{"alias_kind":"pith_short_12","alias_value":"QX5GPKXGUS5I","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QX5GPKXGUS5IK4SM","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QX5GPKXG","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:6a68f437bf9ac7ea141490a8a6ecf01dfe12311f6bdc62f0e04ca23843230958","target":"graph","created_at":"2026-05-17T23:55:42Z","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"},"paper":{"abstract_excerpt":"Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly ","authors_text":"Capital Markets Cooperative Research Centre (CMCRC)), HBKU), Raghavendra Chalapathy (University of Sydney, Sanjay Chawla (Qatar Computing Research Institute (QCRI)","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T21:36:57Z","title":"Deep Learning for Anomaly Detection: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03407","kind":"arxiv","version":2},"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:d7c0fc0c951d14b32b4078009d2388f971d1965e9c0e69816365567befb3834b","target":"record","created_at":"2026-05-17T23:55:42Z","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":"9cb730c9f9e1c5d37c9beb83c99ef6430c7da02a8fff5fced322e60af31aab5b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-10T21:36:57Z","title_canon_sha256":"11143d0e7cf9ba8fab8c0e5738735047997b7da6285d4f5575edbb14102e1a09"},"schema_version":"1.0","source":{"id":"1901.03407","kind":"arxiv","version":2}},"canonical_sha256":"85fa67aae6a4ba85724c64fb12463d6ff1b1e9a53483155f16f9d125cec15ba5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85fa67aae6a4ba85724c64fb12463d6ff1b1e9a53483155f16f9d125cec15ba5","first_computed_at":"2026-05-17T23:55:42.262808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:42.262808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RjDEOnRBCnN7qzakfd0YR2+tIhgpMqjK9uGCI8NM5BbgASnvxrt9Pgms7UxlPfU8Ju7I4+BbvFDPO+EzFoe1Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:42.263419Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.03407","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7c0fc0c951d14b32b4078009d2388f971d1965e9c0e69816365567befb3834b","sha256:6a68f437bf9ac7ea141490a8a6ecf01dfe12311f6bdc62f0e04ca23843230958"],"state_sha256":"d6936a91569ba8502fef36904772617b2adcdf6038cbab1434434bdea21afcfc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yhHHcf3obllN2QXS0g5dfnXRmibLSNPVvxJAH5/CyIknMqwuhJ+ajfBYwqCHDW4TnMEgkFQ9ABwbodfpWqPuBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T07:34:54.137904Z","bundle_sha256":"f53cb23747097d80f398f62574e06eccce254c6e69ad105c76b1614c157b6c30"}}