{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:2VCGLQBBSYKQBMP7FVAY7WFPFI","short_pith_number":"pith:2VCGLQBB","canonical_record":{"source":{"id":"1906.11632","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-27T13:38:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"13ca1f85ee298d70ae45a4c23540bdd1a0fde4120bbd9081177653e2948ad12a","abstract_canon_sha256":"ccce6ad2b1f122ffc82479b428763772ada18e774ec8bd3741d9f8356509128c"},"schema_version":"1.0"},"canonical_sha256":"d54465c021961500b1ff2d418fd8af2a3d164d26b53d0c6b696c393378d8ac71","source":{"kind":"arxiv","id":"1906.11632","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11632","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11632v2","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11632","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_12","alias_value":"2VCGLQBBSYKQ","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_16","alias_value":"2VCGLQBBSYKQBMP7","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_8","alias_value":"2VCGLQBB","created_at":"2026-07-05T03:13:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:2VCGLQBBSYKQBMP7FVAY7WFPFI","target":"record","payload":{"canonical_record":{"source":{"id":"1906.11632","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-27T13:38:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"13ca1f85ee298d70ae45a4c23540bdd1a0fde4120bbd9081177653e2948ad12a","abstract_canon_sha256":"ccce6ad2b1f122ffc82479b428763772ada18e774ec8bd3741d9f8356509128c"},"schema_version":"1.0"},"canonical_sha256":"d54465c021961500b1ff2d418fd8af2a3d164d26b53d0c6b696c393378d8ac71","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:13:46.504289Z","signature_b64":"LA4jYQPUIwZt4NDaGiwDxQ2o7M7bjfjv7yj4JQ5/OrT3ftkG1TwmikVMbCjmNEidrdxraD51XL+bOpWE2k94BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d54465c021961500b1ff2d418fd8af2a3d164d26b53d0c6b696c393378d8ac71","last_reissued_at":"2026-07-05T03:13:46.503734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:13:46.503734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.11632","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":"2026-07-05T03:13:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BjTEtNtceZlu9r7zJE+SuEGH9jwypPWrSqkXu+rhz6upxlbqRnaE56QNcDXgEM5eGtYRmURyCKJwt6Inal9IDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:36:10.697537Z"},"content_sha256":"749f0d65f91266bf8cb06a08cdaa9264f5f5834f057554f714a3396319834d38","schema_version":"1.0","event_id":"sha256:749f0d65f91266bf8cb06a08cdaa9264f5f5834f057554f714a3396319834d38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:2VCGLQBBSYKQBMP7FVAY7WFPFI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on GANs for Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Emanuele Ghelfi, Federico Di Mattia, Michele De Simoni, Paolo Galeone","submitted_at":"2019-06-27T13:38:22Z","abstract_excerpt":"Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years.\n  Generative Adversarial Networks (GANs) and the adversarial training process have been recently employed to face this task yielding remarkable results. In this paper we survey the principal GAN-based anomaly detection methods, highlighting their pros and cons. Our contributions are the empirical validation of the main GAN models for anomaly detection, the increase o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11632","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1906.11632/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:13:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2jWFz+wcCSbK1VNubcw1J1KkB1mGexpmKnYE/qTCn2CO1uHi470AkC6QxcypUc+eYY1xCiiHJHU7lVxHvUXKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:36:10.698478Z"},"content_sha256":"087f75f385485d69b866103949c7b2a8c436bb90a9b163549e5179df4f305644","schema_version":"1.0","event_id":"sha256:087f75f385485d69b866103949c7b2a8c436bb90a9b163549e5179df4f305644"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/bundle.json","state_url":"https://pith.science/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/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":"2026-08-08T07:36:10Z","links":{"resolver":"https://pith.science/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI","bundle":"https://pith.science/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/bundle.json","state":"https://pith.science/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2VCGLQBBSYKQBMP7FVAY7WFPFI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2VCGLQBBSYKQBMP7FVAY7WFPFI","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":"ccce6ad2b1f122ffc82479b428763772ada18e774ec8bd3741d9f8356509128c","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-27T13:38:22Z","title_canon_sha256":"13ca1f85ee298d70ae45a4c23540bdd1a0fde4120bbd9081177653e2948ad12a"},"schema_version":"1.0","source":{"id":"1906.11632","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11632","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11632v2","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11632","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_12","alias_value":"2VCGLQBBSYKQ","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_16","alias_value":"2VCGLQBBSYKQBMP7","created_at":"2026-07-05T03:13:46Z"},{"alias_kind":"pith_short_8","alias_value":"2VCGLQBB","created_at":"2026-07-05T03:13:46Z"}],"graph_snapshots":[{"event_id":"sha256:087f75f385485d69b866103949c7b2a8c436bb90a9b163549e5179df4f305644","target":"graph","created_at":"2026-07-05T03:13:46Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1906.11632/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years.\n  Generative Adversarial Networks (GANs) and the adversarial training process have been recently employed to face this task yielding remarkable results. In this paper we survey the principal GAN-based anomaly detection methods, highlighting their pros and cons. Our contributions are the empirical validation of the main GAN models for anomaly detection, the increase o","authors_text":"Emanuele Ghelfi, Federico Di Mattia, Michele De Simoni, Paolo Galeone","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-27T13:38:22Z","title":"A Survey on GANs for Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11632","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":"sha256:749f0d65f91266bf8cb06a08cdaa9264f5f5834f057554f714a3396319834d38","target":"record","created_at":"2026-07-05T03:13:46Z","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":"ccce6ad2b1f122ffc82479b428763772ada18e774ec8bd3741d9f8356509128c","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-06-27T13:38:22Z","title_canon_sha256":"13ca1f85ee298d70ae45a4c23540bdd1a0fde4120bbd9081177653e2948ad12a"},"schema_version":"1.0","source":{"id":"1906.11632","kind":"arxiv","version":2}},"canonical_sha256":"d54465c021961500b1ff2d418fd8af2a3d164d26b53d0c6b696c393378d8ac71","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d54465c021961500b1ff2d418fd8af2a3d164d26b53d0c6b696c393378d8ac71","first_computed_at":"2026-07-05T03:13:46.503734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:13:46.503734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LA4jYQPUIwZt4NDaGiwDxQ2o7M7bjfjv7yj4JQ5/OrT3ftkG1TwmikVMbCjmNEidrdxraD51XL+bOpWE2k94BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:13:46.504289Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.11632","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:749f0d65f91266bf8cb06a08cdaa9264f5f5834f057554f714a3396319834d38","sha256:087f75f385485d69b866103949c7b2a8c436bb90a9b163549e5179df4f305644"],"state_sha256":"ef0db73bbe3ace7f7b1b827dfd35b1c3a97d6fef99bcac8b6859da5e79c70f82"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sJ5vJZWgTOa+oQLegHgykHxdv0i86Mu+snOPUY9lbsrsq0M1TJZe93RrQRn8lX/kLEDuCxcTyKmP2krzCPJGDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T07:36:10.703793Z","bundle_sha256":"114af1c387e282fd88aa955a908ac9c3423124c9ab509de3aee63857e3dc1c7c"}}