{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4KBPG3XVU2XLTZM3EFXN2WUZ56","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":"1ccd7bc89f99c9cfe54fe24357f558dc02d7bcbc8ea40439467bfe716905426f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T01:44:59Z","title_canon_sha256":"99963a21416f9b92cc63eb1331b6bc207dd72a14b707bedc369c63ff9d76a0cc"},"schema_version":"1.0","source":{"id":"1812.02288","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.02288","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"arxiv_version","alias_value":"1812.02288v1","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02288","created_at":"2026-05-17T23:58:56Z"},{"alias_kind":"pith_short_12","alias_value":"4KBPG3XVU2XL","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4KBPG3XVU2XLTZM3","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4KBPG3XV","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:15dc935546c6863fe35a4db688aa05bb56c38625b503075c45ac24d9fe334e39","target":"graph","created_at":"2026-05-17T23:58:56Z","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 a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction","authors_text":"Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Manon Romain, Vijay Ramaseshan Chandrasekhar","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T01:44:59Z","title":"Adversarially Learned Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02288","kind":"arxiv","version":1},"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:b88deb7ae475461f42c1564e6a39c20aabf7e8119914b2c41125170429f3127e","target":"record","created_at":"2026-05-17T23:58:56Z","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":"1ccd7bc89f99c9cfe54fe24357f558dc02d7bcbc8ea40439467bfe716905426f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-06T01:44:59Z","title_canon_sha256":"99963a21416f9b92cc63eb1331b6bc207dd72a14b707bedc369c63ff9d76a0cc"},"schema_version":"1.0","source":{"id":"1812.02288","kind":"arxiv","version":1}},"canonical_sha256":"e282f36ef5a6aeb9e59b216edd5a99efa064b100fb07e47d04f8215b2b12da3e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e282f36ef5a6aeb9e59b216edd5a99efa064b100fb07e47d04f8215b2b12da3e","first_computed_at":"2026-05-17T23:58:56.678776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:56.678776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y5eC0oWtI/a82O2n8Ly16JmbJtfXjlZHTvdGHC/eCvlzFulCXO44bii7FlVDQ3kGeHclarj1xKEzw8OWmo3hCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:56.679291Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.02288","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b88deb7ae475461f42c1564e6a39c20aabf7e8119914b2c41125170429f3127e","sha256:15dc935546c6863fe35a4db688aa05bb56c38625b503075c45ac24d9fe334e39"],"state_sha256":"9d09600aad61aeb4278a145286a5c04512139a169eb6003544826343221ccab9"}