{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6CKF46X44RPUEICS2CCIMMCBYW","short_pith_number":"pith:6CKF46X4","canonical_record":{"source":{"id":"1811.06861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-16T15:36:28Z","cross_cats_sorted":[],"title_canon_sha256":"8b1a33ba1c57bd8352c8b8966005b7842b07ea111c69b7e34cc28e0307f527ab","abstract_canon_sha256":"548fb954d771580974412134e7e296630bca4fcabd2a45e853bad606efb2ad87"},"schema_version":"1.0"},"canonical_sha256":"f0945e7afce45f422052d084863041c59a4e5760807f806a200b3d8955a55e8b","source":{"kind":"arxiv","id":"1811.06861","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06861","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06861v1","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06861","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"pith_short_12","alias_value":"6CKF46X44RPU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6CKF46X44RPUEICS","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6CKF46X4","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6CKF46X44RPUEICS2CCIMMCBYW","target":"record","payload":{"canonical_record":{"source":{"id":"1811.06861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-16T15:36:28Z","cross_cats_sorted":[],"title_canon_sha256":"8b1a33ba1c57bd8352c8b8966005b7842b07ea111c69b7e34cc28e0307f527ab","abstract_canon_sha256":"548fb954d771580974412134e7e296630bca4fcabd2a45e853bad606efb2ad87"},"schema_version":"1.0"},"canonical_sha256":"f0945e7afce45f422052d084863041c59a4e5760807f806a200b3d8955a55e8b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:33.658099Z","signature_b64":"H9c5YIyhjaXS5pHLydtIS8GZH3IswfhZkHWRkWdb9QQ1GeSg8/wZAXTHeYYMOWWLxPY06hCaQDK62P8w0pQ+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0945e7afce45f422052d084863041c59a4e5760807f806a200b3d8955a55e8b","last_reissued_at":"2026-05-18T00:00:33.657658Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:33.657658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.06861","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":"2026-05-18T00:00:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AcOhNrprZWJywbrFPb5ThRKYABwHvhhRiLZkkbiES6OEOopWA9KpG16I+XL5tZvig+qqxb3poFwxUSFIsQgqCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T07:45:03.380914Z"},"content_sha256":"146c76a20f45a903e61c7d13f7b37fd476d428b08b19c17518fe69a1e1c496e8","schema_version":"1.0","event_id":"sha256:146c76a20f45a903e61c7d13f7b37fd476d428b08b19c17518fe69a1e1c496e8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6CKF46X44RPUEICS2CCIMMCBYW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anomaly Detection using Deep Learning based Image Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dieter P. Gruber, Matthias Haselmann, Paul Tabatabai","submitted_at":"2018-11-16T15:36:28Z","abstract_excerpt":"Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06861","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":""},"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":"2026-05-18T00:00:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lWxSYDLUif6C1DUuSL5Pq1SRI6ZOBlOA4vub7lYzM22uJOuOAZSlFdDlHYFsLoih0BfqT7trbsFaoAH4UIIXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T07:45:03.381252Z"},"content_sha256":"c471f2546227211608157415efafd87d81c9cc47314f8f97b9927994e934e610","schema_version":"1.0","event_id":"sha256:c471f2546227211608157415efafd87d81c9cc47314f8f97b9927994e934e610"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6CKF46X44RPUEICS2CCIMMCBYW/bundle.json","state_url":"https://pith.science/pith/6CKF46X44RPUEICS2CCIMMCBYW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6CKF46X44RPUEICS2CCIMMCBYW/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":"2026-06-24T07:45:03Z","links":{"resolver":"https://pith.science/pith/6CKF46X44RPUEICS2CCIMMCBYW","bundle":"https://pith.science/pith/6CKF46X44RPUEICS2CCIMMCBYW/bundle.json","state":"https://pith.science/pith/6CKF46X44RPUEICS2CCIMMCBYW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6CKF46X44RPUEICS2CCIMMCBYW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6CKF46X44RPUEICS2CCIMMCBYW","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":"548fb954d771580974412134e7e296630bca4fcabd2a45e853bad606efb2ad87","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-16T15:36:28Z","title_canon_sha256":"8b1a33ba1c57bd8352c8b8966005b7842b07ea111c69b7e34cc28e0307f527ab"},"schema_version":"1.0","source":{"id":"1811.06861","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.06861","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"arxiv_version","alias_value":"1811.06861v1","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06861","created_at":"2026-05-18T00:00:33Z"},{"alias_kind":"pith_short_12","alias_value":"6CKF46X44RPU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"6CKF46X44RPUEICS","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"6CKF46X4","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:c471f2546227211608157415efafd87d81c9cc47314f8f97b9927994e934e610","target":"graph","created_at":"2026-05-18T00:00:33Z","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":"Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region","authors_text":"Dieter P. Gruber, Matthias Haselmann, Paul Tabatabai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-16T15:36:28Z","title":"Anomaly Detection using Deep Learning based Image Completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06861","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":"sha256:146c76a20f45a903e61c7d13f7b37fd476d428b08b19c17518fe69a1e1c496e8","target":"record","created_at":"2026-05-18T00:00:33Z","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":"548fb954d771580974412134e7e296630bca4fcabd2a45e853bad606efb2ad87","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-16T15:36:28Z","title_canon_sha256":"8b1a33ba1c57bd8352c8b8966005b7842b07ea111c69b7e34cc28e0307f527ab"},"schema_version":"1.0","source":{"id":"1811.06861","kind":"arxiv","version":1}},"canonical_sha256":"f0945e7afce45f422052d084863041c59a4e5760807f806a200b3d8955a55e8b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0945e7afce45f422052d084863041c59a4e5760807f806a200b3d8955a55e8b","first_computed_at":"2026-05-18T00:00:33.657658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:33.657658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"H9c5YIyhjaXS5pHLydtIS8GZH3IswfhZkHWRkWdb9QQ1GeSg8/wZAXTHeYYMOWWLxPY06hCaQDK62P8w0pQ+Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:33.658099Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.06861","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:146c76a20f45a903e61c7d13f7b37fd476d428b08b19c17518fe69a1e1c496e8","sha256:c471f2546227211608157415efafd87d81c9cc47314f8f97b9927994e934e610"],"state_sha256":"4980e6f9d134160583d0d3047efca00cbe93bb81328feaddb97b6c289696d868"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u1uxpF0WWT+kC9TT70PNKsqhRcF9Yez1ZZJk1zXos1Z5VHRMd4RZuOHjbhiEc2YceqkV5fgUYS3PjuGmpLXBAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T07:45:03.383301Z","bundle_sha256":"000ede70ba375fecb51c2db7988582f288595f541bc65095525f2c12b5bbc8de"}}