{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PDV7B33AEMCZOIKAK6QGMAY2DW","short_pith_number":"pith:PDV7B33A","canonical_record":{"source":{"id":"1807.01136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T13:03:11Z","cross_cats_sorted":[],"title_canon_sha256":"4c2c65921bbebb969feb98c6b1e1e77f2712a71ac7f7cdb711b99efaacd6e103","abstract_canon_sha256":"ffe25a1b946ac971c911793c6f7140cc24f08ccf628897bc03adb1873dd3b620"},"schema_version":"1.0"},"canonical_sha256":"78ebf0ef60230597214057a066031a1d8519a76fd0434266e4632b0b35e092e1","source":{"kind":"arxiv","id":"1807.01136","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01136","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01136v2","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01136","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"PDV7B33AEMCZ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PDV7B33AEMCZOIKA","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PDV7B33A","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PDV7B33AEMCZOIKAK6QGMAY2DW","target":"record","payload":{"canonical_record":{"source":{"id":"1807.01136","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T13:03:11Z","cross_cats_sorted":[],"title_canon_sha256":"4c2c65921bbebb969feb98c6b1e1e77f2712a71ac7f7cdb711b99efaacd6e103","abstract_canon_sha256":"ffe25a1b946ac971c911793c6f7140cc24f08ccf628897bc03adb1873dd3b620"},"schema_version":"1.0"},"canonical_sha256":"78ebf0ef60230597214057a066031a1d8519a76fd0434266e4632b0b35e092e1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:23.237014Z","signature_b64":"yBLmnofaMSvPJ4CBPNoKggwbOIGD5MzEXRXpnZNYDPv0Y7zNwpin2SCZbsf+wlIYGLOR//okniy2nsxpLCrvBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78ebf0ef60230597214057a066031a1d8519a76fd0434266e4632b0b35e092e1","last_reissued_at":"2026-05-18T00:01:23.236545Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:23.236545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.01136","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":"2026-05-18T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hfAHHfHCWnQY7dKHywvfgeOJ4AWdMMaThiOt+iwBn6ML+0qbPTvB+n2zdMK8ZJ1x0z8u+La8Iwlb74GcEu6nDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:51:52.124274Z"},"content_sha256":"b63a237f7b4d78253a9636254d7097df5c1b86ebe31f16ccd3c5ad52ca3ed1ac","schema_version":"1.0","event_id":"sha256:b63a237f7b4d78253a9636254d7097df5c1b86ebe31f16ccd3c5ad52ca3ed1ac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PDV7B33AEMCZOIKAK6QGMAY2DW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Masanari Kimura, Takashi Yanagihara","submitted_at":"2018-07-03T13:03:11Z","abstract_excerpt":"The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01136","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":"2026-05-18T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gsu6puGK9RHk7bLcqMRUpDoJKhb+8pO/gWvX5dB0yLA/+DQrnrFD77R3tHWbsBhy/a9QZ2h9cvqJd9egNi7bDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:51:52.125217Z"},"content_sha256":"ea96eff00d6b6eaef0a09ee109a351b77d64f2801ce4e2b567198db117108c1b","schema_version":"1.0","event_id":"sha256:ea96eff00d6b6eaef0a09ee109a351b77d64f2801ce4e2b567198db117108c1b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/bundle.json","state_url":"https://pith.science/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/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":"2026-08-07T03:51:52Z","links":{"resolver":"https://pith.science/pith/PDV7B33AEMCZOIKAK6QGMAY2DW","bundle":"https://pith.science/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/bundle.json","state":"https://pith.science/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PDV7B33AEMCZOIKAK6QGMAY2DW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PDV7B33AEMCZOIKAK6QGMAY2DW","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":"ffe25a1b946ac971c911793c6f7140cc24f08ccf628897bc03adb1873dd3b620","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T13:03:11Z","title_canon_sha256":"4c2c65921bbebb969feb98c6b1e1e77f2712a71ac7f7cdb711b99efaacd6e103"},"schema_version":"1.0","source":{"id":"1807.01136","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01136","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01136v2","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01136","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"PDV7B33AEMCZ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PDV7B33AEMCZOIKA","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PDV7B33A","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:ea96eff00d6b6eaef0a09ee109a351b77d64f2801ce4e2b567198db117108c1b","target":"graph","created_at":"2026-05-18T00:01:23Z","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":"The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit t","authors_text":"Masanari Kimura, Takashi Yanagihara","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T13:03:11Z","title":"Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01136","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:b63a237f7b4d78253a9636254d7097df5c1b86ebe31f16ccd3c5ad52ca3ed1ac","target":"record","created_at":"2026-05-18T00:01:23Z","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":"ffe25a1b946ac971c911793c6f7140cc24f08ccf628897bc03adb1873dd3b620","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-03T13:03:11Z","title_canon_sha256":"4c2c65921bbebb969feb98c6b1e1e77f2712a71ac7f7cdb711b99efaacd6e103"},"schema_version":"1.0","source":{"id":"1807.01136","kind":"arxiv","version":2}},"canonical_sha256":"78ebf0ef60230597214057a066031a1d8519a76fd0434266e4632b0b35e092e1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78ebf0ef60230597214057a066031a1d8519a76fd0434266e4632b0b35e092e1","first_computed_at":"2026-05-18T00:01:23.236545Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:23.236545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yBLmnofaMSvPJ4CBPNoKggwbOIGD5MzEXRXpnZNYDPv0Y7zNwpin2SCZbsf+wlIYGLOR//okniy2nsxpLCrvBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:23.237014Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.01136","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b63a237f7b4d78253a9636254d7097df5c1b86ebe31f16ccd3c5ad52ca3ed1ac","sha256:ea96eff00d6b6eaef0a09ee109a351b77d64f2801ce4e2b567198db117108c1b"],"state_sha256":"49c2ae7cc63f1851f4a92dda4f32ec970c36f91c9d8741b23e61e2a56c264fbb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GlSEa9K1dgQMi7ynbxQI59L+P87FIEZwuxMlLnW+O/y4tY22xAXN2Bz1tcDbPcb7uOHUAGB8jB3yJSFpbE8KCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:51:52.137049Z","bundle_sha256":"af7edb7bf4d905b839e4804914c9e9976c849d3a74305be4c38b4f696206eae7"}}