{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QFN77AWW7RBGN63RAXWK6EBY7S","short_pith_number":"pith:QFN77AWW","canonical_record":{"source":{"id":"1906.11876","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-29T13:44:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1323a18f2ac86fba436e68315c3670a16adb5948e0de8e79aa11dba2e60a24b3","abstract_canon_sha256":"7aaff35e5fa2d52108a90644c8bcec31b43f28171e1b324e6974077b5e3fda0f"},"schema_version":"1.0"},"canonical_sha256":"815bff82d6fc4266fb7105ecaf1038fcbcdbd9a5a9d4ad7667c2da20f708687d","source":{"kind":"arxiv","id":"1906.11876","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11876","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11876v1","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11876","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"pith_short_12","alias_value":"QFN77AWW7RBG","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QFN77AWW7RBGN63R","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QFN77AWW","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QFN77AWW7RBGN63RAXWK6EBY7S","target":"record","payload":{"canonical_record":{"source":{"id":"1906.11876","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-29T13:44:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1323a18f2ac86fba436e68315c3670a16adb5948e0de8e79aa11dba2e60a24b3","abstract_canon_sha256":"7aaff35e5fa2d52108a90644c8bcec31b43f28171e1b324e6974077b5e3fda0f"},"schema_version":"1.0"},"canonical_sha256":"815bff82d6fc4266fb7105ecaf1038fcbcdbd9a5a9d4ad7667c2da20f708687d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:01.800537Z","signature_b64":"k/NEg99ImfVgrwTfDseJF6k06yXUk2qn32DJ9YyCAphiD6oJZNdodQCEF4p27Bj+X2DMJ9cLKHYOe93xJnzPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"815bff82d6fc4266fb7105ecaf1038fcbcdbd9a5a9d4ad7667c2da20f708687d","last_reissued_at":"2026-05-17T23:42:01.799940Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:01.799940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.11876","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":"2026-05-17T23:42:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g+foiHUiq72sSG4trM23M1xM+BK1ZohrfUAhGz/g1T7Cm/GZ86nfrEoIekif/H/vxCFSDvI2zTW+8aVe2N3OCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T05:05:52.003968Z"},"content_sha256":"7c7909e1e59c586cdd77b5d16e3bb627a1ce31a4fb184e572bf4b5fbff0b9933","schema_version":"1.0","event_id":"sha256:7c7909e1e59c586cdd77b5d16e3bb627a1ce31a4fb184e572bf4b5fbff0b9933"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QFN77AWW7RBGN63RAXWK6EBY7S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty Based Detection and Relabeling of Noisy Image Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Jan M.K\\\"ohler, Maximilian Autenrieth, William H. Beluch","submitted_at":"2019-05-29T13:44:38Z","abstract_excerpt":"Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11876","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":"2026-05-17T23:42:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ZZQtnSoam0/6tEIqVxIK+n6jAFq7YA+9YNr6vDhnJZJf8C8KCQFYOSDOPAq7cSa+VcnfQU/XlYVI2ePsx2OCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T05:05:52.004392Z"},"content_sha256":"29214a73bf8a5fef8de2939ed9fae3e756e56ae674b5efeb95384633c141c141","schema_version":"1.0","event_id":"sha256:29214a73bf8a5fef8de2939ed9fae3e756e56ae674b5efeb95384633c141c141"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QFN77AWW7RBGN63RAXWK6EBY7S/bundle.json","state_url":"https://pith.science/pith/QFN77AWW7RBGN63RAXWK6EBY7S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QFN77AWW7RBGN63RAXWK6EBY7S/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":"2026-05-23T05:05:52Z","links":{"resolver":"https://pith.science/pith/QFN77AWW7RBGN63RAXWK6EBY7S","bundle":"https://pith.science/pith/QFN77AWW7RBGN63RAXWK6EBY7S/bundle.json","state":"https://pith.science/pith/QFN77AWW7RBGN63RAXWK6EBY7S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QFN77AWW7RBGN63RAXWK6EBY7S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QFN77AWW7RBGN63RAXWK6EBY7S","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":"7aaff35e5fa2d52108a90644c8bcec31b43f28171e1b324e6974077b5e3fda0f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-29T13:44:38Z","title_canon_sha256":"1323a18f2ac86fba436e68315c3670a16adb5948e0de8e79aa11dba2e60a24b3"},"schema_version":"1.0","source":{"id":"1906.11876","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11876","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11876v1","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11876","created_at":"2026-05-17T23:42:01Z"},{"alias_kind":"pith_short_12","alias_value":"QFN77AWW7RBG","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QFN77AWW7RBGN63R","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QFN77AWW","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:29214a73bf8a5fef8de2939ed9fae3e756e56ae674b5efeb95384633c141c141","target":"graph","created_at":"2026-05-17T23:42:01Z","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":"Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. D","authors_text":"Jan M.K\\\"ohler, Maximilian Autenrieth, William H. Beluch","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-29T13:44:38Z","title":"Uncertainty Based Detection and Relabeling of Noisy Image Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11876","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":"sha256:7c7909e1e59c586cdd77b5d16e3bb627a1ce31a4fb184e572bf4b5fbff0b9933","target":"record","created_at":"2026-05-17T23:42:01Z","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":"7aaff35e5fa2d52108a90644c8bcec31b43f28171e1b324e6974077b5e3fda0f","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-29T13:44:38Z","title_canon_sha256":"1323a18f2ac86fba436e68315c3670a16adb5948e0de8e79aa11dba2e60a24b3"},"schema_version":"1.0","source":{"id":"1906.11876","kind":"arxiv","version":1}},"canonical_sha256":"815bff82d6fc4266fb7105ecaf1038fcbcdbd9a5a9d4ad7667c2da20f708687d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"815bff82d6fc4266fb7105ecaf1038fcbcdbd9a5a9d4ad7667c2da20f708687d","first_computed_at":"2026-05-17T23:42:01.799940Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:01.799940Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k/NEg99ImfVgrwTfDseJF6k06yXUk2qn32DJ9YyCAphiD6oJZNdodQCEF4p27Bj+X2DMJ9cLKHYOe93xJnzPAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:01.800537Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.11876","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c7909e1e59c586cdd77b5d16e3bb627a1ce31a4fb184e572bf4b5fbff0b9933","sha256:29214a73bf8a5fef8de2939ed9fae3e756e56ae674b5efeb95384633c141c141"],"state_sha256":"cd4a46a33d4e4495fa2024839fc52c1f90280f8feeb65d2230f698f7524a927a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e+D/uZ8aRGgxxBCcMqqaUK0SjYlbor5xs3mGbJ03V72OomkzVT4KvtkgY5smT82esyIRyChLF6Ebv5ICgfWHDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T05:05:52.007408Z","bundle_sha256":"9b543a5882a428bca51cfab183c6492dcbed23c90b157723d2f69789b72976be"}}