{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LZ3FANFUMVJWKWSIXHNKOFKDVS","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":"28b508cefbf325f03786446e404cb6f1b26219905d445cd6a3c7ff7ca955f848","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-04T14:28:52Z","title_canon_sha256":"40130e45d3d4ecc069c7044f6f014e833522aead4bf786c4d482e873e0f007b2"},"schema_version":"1.0","source":{"id":"2407.03961","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.03961","created_at":"2026-07-05T08:42:38Z"},{"alias_kind":"arxiv_version","alias_value":"2407.03961v2","created_at":"2026-07-05T08:42:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03961","created_at":"2026-07-05T08:42:38Z"},{"alias_kind":"pith_short_12","alias_value":"LZ3FANFUMVJW","created_at":"2026-07-05T08:42:38Z"},{"alias_kind":"pith_short_16","alias_value":"LZ3FANFUMVJWKWSI","created_at":"2026-07-05T08:42:38Z"},{"alias_kind":"pith_short_8","alias_value":"LZ3FANFU","created_at":"2026-07-05T08:42:38Z"}],"graph_snapshots":[{"event_id":"sha256:e513e3168dc00bf06d873ee8f3e3cec24e24f572db899c457024c54e8397fd8c","target":"graph","created_at":"2026-07-05T08:42:38Z","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/2407.03961/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. State-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples to mitigate problems related to unbalanced training data. These techniques often produce out-of-distribution images, resulting in systems that learn what is not a normal sample but cannot accur","authors_text":"Federico Girella, Francesco Setti, Franco Fummi, Luigi Capogrosso, Marco Cristani, Ziyue Liu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-04T14:28:52Z","title":"Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03961","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:f38c30509b73753618829e01d8d294d41340cf580ce04dfccc78002b0b89b181","target":"record","created_at":"2026-07-05T08:42:38Z","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":"28b508cefbf325f03786446e404cb6f1b26219905d445cd6a3c7ff7ca955f848","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-04T14:28:52Z","title_canon_sha256":"40130e45d3d4ecc069c7044f6f014e833522aead4bf786c4d482e873e0f007b2"},"schema_version":"1.0","source":{"id":"2407.03961","kind":"arxiv","version":2}},"canonical_sha256":"5e765034b46553655a48b9daa71543ac8b39c4335aabc722eb9f362634c2f8d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e765034b46553655a48b9daa71543ac8b39c4335aabc722eb9f362634c2f8d3","first_computed_at":"2026-07-05T08:42:38.164359Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:42:38.164359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2li9kU4wAFqDu7uTPKB1oSVvaBcJeklZX892+kq3Y18icCQcIdCf6aZpc8pz52TcSMLRZRXtxNNBb1Tw8s/YDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:42:38.164809Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.03961","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f38c30509b73753618829e01d8d294d41340cf580ce04dfccc78002b0b89b181","sha256:e513e3168dc00bf06d873ee8f3e3cec24e24f572db899c457024c54e8397fd8c"],"state_sha256":"d65573b6a53a5a858ae10f950dfff4c245fa9f1ac5af39e036cc4b1e7f65c67d"}