{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KZ6WQEHK6OPPT7GJNTG7CMFE2V","short_pith_number":"pith:KZ6WQEHK","canonical_record":{"source":{"id":"2305.16713","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T07:59:36Z","cross_cats_sorted":[],"title_canon_sha256":"15133bc9998c34ac0e9f2e549fe7772f25e5ea113f3ed5c70c32e38112b0962c","abstract_canon_sha256":"e1e3bd99625c6cc4b3f27c063b526394afe0ba86f626f91782a6e16a86a1827c"},"schema_version":"1.0"},"canonical_sha256":"567d6810eaf39ef9fcc96ccdf130a4d54179f3abb553b6cd27a59f05a8bb332c","source":{"kind":"arxiv","id":"2305.16713","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16713","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16713v3","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16713","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_12","alias_value":"KZ6WQEHK6OPP","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_16","alias_value":"KZ6WQEHK6OPPT7GJ","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_8","alias_value":"KZ6WQEHK","created_at":"2026-07-05T07:31:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KZ6WQEHK6OPPT7GJNTG7CMFE2V","target":"record","payload":{"canonical_record":{"source":{"id":"2305.16713","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T07:59:36Z","cross_cats_sorted":[],"title_canon_sha256":"15133bc9998c34ac0e9f2e549fe7772f25e5ea113f3ed5c70c32e38112b0962c","abstract_canon_sha256":"e1e3bd99625c6cc4b3f27c063b526394afe0ba86f626f91782a6e16a86a1827c"},"schema_version":"1.0"},"canonical_sha256":"567d6810eaf39ef9fcc96ccdf130a4d54179f3abb553b6cd27a59f05a8bb332c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:52.458252Z","signature_b64":"j6+uzS+6q4ofWo3mQ+cW7UY9iTRkdQoLWPxbew9doSeSGlnWszh2WxQkKLZEvnHAWzMv7793t1zA4vhE+gfiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"567d6810eaf39ef9fcc96ccdf130a4d54179f3abb553b6cd27a59f05a8bb332c","last_reissued_at":"2026-07-05T07:31:52.457667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:52.457667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.16713","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:31:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IiCrf88MVBi4L7glDzlEBxhTrpJLwxbcJFrCcVUkPAT8WSjiOgWKNBcozaRaN/nv7mjgzb2dsQiL4aKRUAP7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T02:46:26.370926Z"},"content_sha256":"4d5060d48c82400e4353f8a144b0453cdb2ca8191e4fb58b02378b2d49269638","schema_version":"1.0","event_id":"sha256:4d5060d48c82400e4353f8a144b0453cdb2ca8191e4fb58b02378b2d49269638"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KZ6WQEHK6OPPT7GJNTG7CMFE2V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Byung Jun Kang, Giyoung Jeon, Jeeho Hyun, Kyunghoon Bae, Sangyun Kim, Seung Hwan Kim","submitted_at":"2023-05-26T07:59:36Z","abstract_excerpt":"Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmenta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16713","kind":"arxiv","version":3},"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/2305.16713/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":"2026-07-05T07:31:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z2vssrcYzzHNdhBllZGJoiCS9c10P7DnPoJdsUUusH3WEbWu/5toxBGozCqGBQ7GenBWszJ9fdzDhqWfGQKAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T02:46:26.371862Z"},"content_sha256":"abeefd104e7cebf676aa7bc7543a834f762da76ebc2d6d28a94c150231714442","schema_version":"1.0","event_id":"sha256:abeefd104e7cebf676aa7bc7543a834f762da76ebc2d6d28a94c150231714442"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/bundle.json","state_url":"https://pith.science/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/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":"2026-08-14T02:46:26Z","links":{"resolver":"https://pith.science/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V","bundle":"https://pith.science/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/bundle.json","state":"https://pith.science/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZ6WQEHK6OPPT7GJNTG7CMFE2V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KZ6WQEHK6OPPT7GJNTG7CMFE2V","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":"e1e3bd99625c6cc4b3f27c063b526394afe0ba86f626f91782a6e16a86a1827c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T07:59:36Z","title_canon_sha256":"15133bc9998c34ac0e9f2e549fe7772f25e5ea113f3ed5c70c32e38112b0962c"},"schema_version":"1.0","source":{"id":"2305.16713","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16713","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16713v3","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16713","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_12","alias_value":"KZ6WQEHK6OPP","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_16","alias_value":"KZ6WQEHK6OPPT7GJ","created_at":"2026-07-05T07:31:52Z"},{"alias_kind":"pith_short_8","alias_value":"KZ6WQEHK","created_at":"2026-07-05T07:31:52Z"}],"graph_snapshots":[{"event_id":"sha256:abeefd104e7cebf676aa7bc7543a834f762da76ebc2d6d28a94c150231714442","target":"graph","created_at":"2026-07-05T07:31:52Z","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/2305.16713/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmenta","authors_text":"Byung Jun Kang, Giyoung Jeon, Jeeho Hyun, Kyunghoon Bae, Sangyun Kim, Seung Hwan Kim","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T07:59:36Z","title":"ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16713","kind":"arxiv","version":3},"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":"sha256:4d5060d48c82400e4353f8a144b0453cdb2ca8191e4fb58b02378b2d49269638","target":"record","created_at":"2026-07-05T07:31:52Z","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":"e1e3bd99625c6cc4b3f27c063b526394afe0ba86f626f91782a6e16a86a1827c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T07:59:36Z","title_canon_sha256":"15133bc9998c34ac0e9f2e549fe7772f25e5ea113f3ed5c70c32e38112b0962c"},"schema_version":"1.0","source":{"id":"2305.16713","kind":"arxiv","version":3}},"canonical_sha256":"567d6810eaf39ef9fcc96ccdf130a4d54179f3abb553b6cd27a59f05a8bb332c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"567d6810eaf39ef9fcc96ccdf130a4d54179f3abb553b6cd27a59f05a8bb332c","first_computed_at":"2026-07-05T07:31:52.457667Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:31:52.457667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j6+uzS+6q4ofWo3mQ+cW7UY9iTRkdQoLWPxbew9doSeSGlnWszh2WxQkKLZEvnHAWzMv7793t1zA4vhE+gfiBA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:31:52.458252Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.16713","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d5060d48c82400e4353f8a144b0453cdb2ca8191e4fb58b02378b2d49269638","sha256:abeefd104e7cebf676aa7bc7543a834f762da76ebc2d6d28a94c150231714442"],"state_sha256":"c9ace79052fbc54835644ab7373191640ea9480e575c007a16aa1bb5fc2d6fac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XqIBqbcXsg1vwI6WIdWxFQ3VXM3Fwk7nVMMbuZsOqkcKYTqvmLbYVge5c0WACMddnVPXf8qJmzyxJvSSGKndCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T02:46:26.380084Z","bundle_sha256":"b025f6cd63dc82b9bfef1a945ccfb482d6ea3563f718634910457329f5c63321"}}