{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:SUALEANUU4LLXSHF3AQGSWCZK7","short_pith_number":"pith:SUALEANU","canonical_record":{"source":{"id":"2005.02359","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T17:44:40Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"b6f4ba9c34291144c7a6a96992b5f2fdc803c52fab4f68abb99a2e4b5f74d4b5","abstract_canon_sha256":"ed455f7d983e3f97ccea4be09615483b1d1272f81e06f373fca1e54efca20d15"},"schema_version":"1.0"},"canonical_sha256":"9500b201b4a716bbc8e5d82069585957f38ad031e8e6f8f1aa59ff87d1e82623","source":{"kind":"arxiv","id":"2005.02359","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.02359","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"arxiv_version","alias_value":"2005.02359v1","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.02359","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_12","alias_value":"SUALEANUU4LL","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_16","alias_value":"SUALEANUU4LLXSHF","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_8","alias_value":"SUALEANU","created_at":"2026-07-05T01:00:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:SUALEANUU4LLXSHF3AQGSWCZK7","target":"record","payload":{"canonical_record":{"source":{"id":"2005.02359","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T17:44:40Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"b6f4ba9c34291144c7a6a96992b5f2fdc803c52fab4f68abb99a2e4b5f74d4b5","abstract_canon_sha256":"ed455f7d983e3f97ccea4be09615483b1d1272f81e06f373fca1e54efca20d15"},"schema_version":"1.0"},"canonical_sha256":"9500b201b4a716bbc8e5d82069585957f38ad031e8e6f8f1aa59ff87d1e82623","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:25.156933Z","signature_b64":"LBt07dows9YQfP6cYHpcHojMuq8/J1hSI8Zd5K5iRYAXjAzLRa61lTDZYyuCyBjgryYE6r81A8WucOJ/uE8VAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9500b201b4a716bbc8e5d82069585957f38ad031e8e6f8f1aa59ff87d1e82623","last_reissued_at":"2026-07-05T01:00:25.156424Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:25.156424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.02359","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:00:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bwfQ/Xdj0R0w5fuJi6d6X2LTN/bciCImPtMXzgnSPrV5JmElSwGq+8hfj0DSmDX6USOSA9C2N8YZV5dU0RHCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:05:35.467430Z"},"content_sha256":"79836e72d98ab5b886514815cde1c45e697698db6325b43cab9916015ead873f","schema_version":"1.0","event_id":"sha256:79836e72d98ab5b886514815cde1c45e697698db6325b43cab9916015ead873f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:SUALEANUU4LLXSHF3AQGSWCZK7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classification-Based Anomaly Detection for General Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Liron Bergman, Yedid Hoshen","submitted_at":"2020-05-05T17:44:40Z","abstract_excerpt":"Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.02359","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.02359/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":"2026-07-05T01:00:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gkBg3mY/6HXiFUntOYCuukby/sGm51booIagbCRAt2O1WJuNpgOcdiuuWK9i7Y+GIHGi5sT1+tRJo/7UnbMNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:05:35.467949Z"},"content_sha256":"2a88c2f76f352484fdcf4c0d2536db25c391e48a002590a1354208929a89080a","schema_version":"1.0","event_id":"sha256:2a88c2f76f352484fdcf4c0d2536db25c391e48a002590a1354208929a89080a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SUALEANUU4LLXSHF3AQGSWCZK7/bundle.json","state_url":"https://pith.science/pith/SUALEANUU4LLXSHF3AQGSWCZK7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SUALEANUU4LLXSHF3AQGSWCZK7/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":"2026-08-07T18:05:35Z","links":{"resolver":"https://pith.science/pith/SUALEANUU4LLXSHF3AQGSWCZK7","bundle":"https://pith.science/pith/SUALEANUU4LLXSHF3AQGSWCZK7/bundle.json","state":"https://pith.science/pith/SUALEANUU4LLXSHF3AQGSWCZK7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SUALEANUU4LLXSHF3AQGSWCZK7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:SUALEANUU4LLXSHF3AQGSWCZK7","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":"ed455f7d983e3f97ccea4be09615483b1d1272f81e06f373fca1e54efca20d15","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T17:44:40Z","title_canon_sha256":"b6f4ba9c34291144c7a6a96992b5f2fdc803c52fab4f68abb99a2e4b5f74d4b5"},"schema_version":"1.0","source":{"id":"2005.02359","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.02359","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"arxiv_version","alias_value":"2005.02359v1","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.02359","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_12","alias_value":"SUALEANUU4LL","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_16","alias_value":"SUALEANUU4LLXSHF","created_at":"2026-07-05T01:00:25Z"},{"alias_kind":"pith_short_8","alias_value":"SUALEANU","created_at":"2026-07-05T01:00:25Z"}],"graph_snapshots":[{"event_id":"sha256:2a88c2f76f352484fdcf4c0d2536db25c391e48a002590a1354208929a89080a","target":"graph","created_at":"2026-07-05T01:00:25Z","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/2005.02359/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The str","authors_text":"Liron Bergman, Yedid Hoshen","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T17:44:40Z","title":"Classification-Based Anomaly Detection for General Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.02359","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":"sha256:79836e72d98ab5b886514815cde1c45e697698db6325b43cab9916015ead873f","target":"record","created_at":"2026-07-05T01:00:25Z","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":"ed455f7d983e3f97ccea4be09615483b1d1272f81e06f373fca1e54efca20d15","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T17:44:40Z","title_canon_sha256":"b6f4ba9c34291144c7a6a96992b5f2fdc803c52fab4f68abb99a2e4b5f74d4b5"},"schema_version":"1.0","source":{"id":"2005.02359","kind":"arxiv","version":1}},"canonical_sha256":"9500b201b4a716bbc8e5d82069585957f38ad031e8e6f8f1aa59ff87d1e82623","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9500b201b4a716bbc8e5d82069585957f38ad031e8e6f8f1aa59ff87d1e82623","first_computed_at":"2026-07-05T01:00:25.156424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:00:25.156424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LBt07dows9YQfP6cYHpcHojMuq8/J1hSI8Zd5K5iRYAXjAzLRa61lTDZYyuCyBjgryYE6r81A8WucOJ/uE8VAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:00:25.156933Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.02359","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79836e72d98ab5b886514815cde1c45e697698db6325b43cab9916015ead873f","sha256:2a88c2f76f352484fdcf4c0d2536db25c391e48a002590a1354208929a89080a"],"state_sha256":"0db69149671d616c628a7cc74044e2ba7c9375fd5ade875b3124e4e39cc91bba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G+K+W6lElD65OtJyPXojRlHE1LDjls5GlPDHF+0Bfh8J8L15xeGozqYj7013fO1EMpoOjag3H0d6PHTJlcbgDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:05:35.472327Z","bundle_sha256":"6fb52f5106c87bee917938072db1ba58f0dc725403c7409878dcb8d7c131cbaf"}}