{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:P2GBWBJMCHB4JY7N4RUAVDXFYZ","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":"6e2eb2eb5a5b50ff4bc1e4ab775e3b07343438a04621dc0356353bd68c50ba05","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-06T16:46:56Z","title_canon_sha256":"54396b4b2dde3055f7a484f48183b76827c97446135941b946fa51195e70c837"},"schema_version":"1.0","source":{"id":"1906.02694","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02694","created_at":"2026-07-05T00:40:43Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02694v2","created_at":"2026-07-05T00:40:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02694","created_at":"2026-07-05T00:40:43Z"},{"alias_kind":"pith_short_12","alias_value":"P2GBWBJMCHB4","created_at":"2026-07-05T00:40:43Z"},{"alias_kind":"pith_short_16","alias_value":"P2GBWBJMCHB4JY7N","created_at":"2026-07-05T00:40:43Z"},{"alias_kind":"pith_short_8","alias_value":"P2GBWBJM","created_at":"2026-07-05T00:40:43Z"}],"graph_snapshots":[{"event_id":"sha256:5ce64798c719b8f940404ac45ea3d800d4a2a2e723eaa3b3d84ce8976ed88271","target":"graph","created_at":"2026-07-05T00:40:43Z","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/1906.02694/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take ad","authors_text":"Alexander Binder, Emmanuel M\\\"uller, Klaus-Robert M\\\"uller, Lukas Ruff, Marius Kloft, Nico G\\\"ornitz, Robert A. Vandermeulen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-06T16:46:56Z","title":"Deep Semi-Supervised Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02694","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:ceb2e058f0bd306b4c709e249e1d0f393f6f8e87a076609270336e88b4bd696e","target":"record","created_at":"2026-07-05T00:40:43Z","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":"6e2eb2eb5a5b50ff4bc1e4ab775e3b07343438a04621dc0356353bd68c50ba05","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-06T16:46:56Z","title_canon_sha256":"54396b4b2dde3055f7a484f48183b76827c97446135941b946fa51195e70c837"},"schema_version":"1.0","source":{"id":"1906.02694","kind":"arxiv","version":2}},"canonical_sha256":"7e8c1b052c11c3c4e3ede4680a8ee5c65f64c40460482989f7cd05ced006afe6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e8c1b052c11c3c4e3ede4680a8ee5c65f64c40460482989f7cd05ced006afe6","first_computed_at":"2026-07-05T00:40:43.131974Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:40:43.131974Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"92yiNuDHNn69YMk/n9nX9beIP+0RCjIQO38bD5KVrsaa7k2NkazoZVGZKtnmkCd2jYAV+d13JNbDBFYfl5SqDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:40:43.132485Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.02694","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ceb2e058f0bd306b4c709e249e1d0f393f6f8e87a076609270336e88b4bd696e","sha256:5ce64798c719b8f940404ac45ea3d800d4a2a2e723eaa3b3d84ce8976ed88271"],"state_sha256":"45a198f78811e69d9d259bffe400116c652620dbe637e8517e0a95a17996e2d7"}