{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:KBQZ5ZM5PJIMHVCKRXFINEDVX4","short_pith_number":"pith:KBQZ5ZM5","canonical_record":{"source":{"id":"2002.02705","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T10:42:26Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"2d5e78a14a1e4fa8948f15bc32d22e2f2e9d7ceea696bc24e99d454dc8b6e691","abstract_canon_sha256":"154bb44fe5b1df640d9f91007bf20de2ad798b733b6360b24ba66846d18dab22"},"schema_version":"1.0"},"canonical_sha256":"50619ee59d7a50c3d44a8dca869075bf0b00dba510ab76521ea0b2e44d14ba5c","source":{"kind":"arxiv","id":"2002.02705","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.02705","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2002.02705v3","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.02705","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"KBQZ5ZM5PJIM","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"KBQZ5ZM5PJIMHVCK","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"KBQZ5ZM5","created_at":"2026-07-05T01:19:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:KBQZ5ZM5PJIMHVCKRXFINEDVX4","target":"record","payload":{"canonical_record":{"source":{"id":"2002.02705","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T10:42:26Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"2d5e78a14a1e4fa8948f15bc32d22e2f2e9d7ceea696bc24e99d454dc8b6e691","abstract_canon_sha256":"154bb44fe5b1df640d9f91007bf20de2ad798b733b6360b24ba66846d18dab22"},"schema_version":"1.0"},"canonical_sha256":"50619ee59d7a50c3d44a8dca869075bf0b00dba510ab76521ea0b2e44d14ba5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:51.636257Z","signature_b64":"P0QmK2JocoRBxfrdR9nmBC0zf+iLO4BIXInWRWjrus2RRkoLMK8kxGZu30R24ORTHVRr5mQq0WPVpJ8w/FHdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50619ee59d7a50c3d44a8dca869075bf0b00dba510ab76521ea0b2e44d14ba5c","last_reissued_at":"2026-07-05T01:19:51.635820Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:51.635820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.02705","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":"2026-07-05T01:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2rNsSPLuMqNZ4YNETN9dFAokZPE7U6fgR2QqUk9yz6RxaR4QfWQ2qNwiJqUWfBSX6vTRzwGkNLT95NT57d8RAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:37:58.011941Z"},"content_sha256":"64f51c7f3d7ea953891f7355d25c16d83687498e4261080eb130f9d3842294a5","schema_version":"1.0","event_id":"sha256:64f51c7f3d7ea953891f7355d25c16d83687498e4261080eb130f9d3842294a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:KBQZ5ZM5PJIMHVCKRXFINEDVX4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bernhard Sick, Christian Haase-Sch\\\"utz, Heinz Hertlein, Rainer Stal","submitted_at":"2020-02-07T10:42:26Z","abstract_excerpt":"State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to label errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of iterative training "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.02705","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/2002.02705/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":"2026-07-05T01:19:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CGkyYGnhM3YurVPDWCEVHcQMFYi7ctZkawJp+cf/h67qnQxgC4iICW3/rSaLy/Gdkkc6NFoFPgxczFSuyjE1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:37:58.012334Z"},"content_sha256":"8b785f3b4206a4b5bd1717529dcbc44924b21eafaf32a6d2fc75b920fa99fe7f","schema_version":"1.0","event_id":"sha256:8b785f3b4206a4b5bd1717529dcbc44924b21eafaf32a6d2fc75b920fa99fe7f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/bundle.json","state_url":"https://pith.science/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/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":"2026-08-03T18:37:58Z","links":{"resolver":"https://pith.science/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4","bundle":"https://pith.science/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/bundle.json","state":"https://pith.science/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KBQZ5ZM5PJIMHVCKRXFINEDVX4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KBQZ5ZM5PJIMHVCKRXFINEDVX4","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":"154bb44fe5b1df640d9f91007bf20de2ad798b733b6360b24ba66846d18dab22","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T10:42:26Z","title_canon_sha256":"2d5e78a14a1e4fa8948f15bc32d22e2f2e9d7ceea696bc24e99d454dc8b6e691"},"schema_version":"1.0","source":{"id":"2002.02705","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.02705","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"arxiv_version","alias_value":"2002.02705v3","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.02705","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_12","alias_value":"KBQZ5ZM5PJIM","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_16","alias_value":"KBQZ5ZM5PJIMHVCK","created_at":"2026-07-05T01:19:51Z"},{"alias_kind":"pith_short_8","alias_value":"KBQZ5ZM5","created_at":"2026-07-05T01:19:51Z"}],"graph_snapshots":[{"event_id":"sha256:8b785f3b4206a4b5bd1717529dcbc44924b21eafaf32a6d2fc75b920fa99fe7f","target":"graph","created_at":"2026-07-05T01:19:51Z","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/2002.02705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to label errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of iterative training ","authors_text":"Bernhard Sick, Christian Haase-Sch\\\"utz, Heinz Hertlein, Rainer Stal","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T10:42:26Z","title":"Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.02705","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":"sha256:64f51c7f3d7ea953891f7355d25c16d83687498e4261080eb130f9d3842294a5","target":"record","created_at":"2026-07-05T01:19:51Z","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":"154bb44fe5b1df640d9f91007bf20de2ad798b733b6360b24ba66846d18dab22","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-07T10:42:26Z","title_canon_sha256":"2d5e78a14a1e4fa8948f15bc32d22e2f2e9d7ceea696bc24e99d454dc8b6e691"},"schema_version":"1.0","source":{"id":"2002.02705","kind":"arxiv","version":3}},"canonical_sha256":"50619ee59d7a50c3d44a8dca869075bf0b00dba510ab76521ea0b2e44d14ba5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"50619ee59d7a50c3d44a8dca869075bf0b00dba510ab76521ea0b2e44d14ba5c","first_computed_at":"2026-07-05T01:19:51.635820Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:19:51.635820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P0QmK2JocoRBxfrdR9nmBC0zf+iLO4BIXInWRWjrus2RRkoLMK8kxGZu30R24ORTHVRr5mQq0WPVpJ8w/FHdCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:19:51.636257Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.02705","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64f51c7f3d7ea953891f7355d25c16d83687498e4261080eb130f9d3842294a5","sha256:8b785f3b4206a4b5bd1717529dcbc44924b21eafaf32a6d2fc75b920fa99fe7f"],"state_sha256":"150097b657f1074b9ceb094d53c494c85adcae6efd968f2c1d516425a08726ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0hNjofYRcZFme2mogWzDDLu4EZRP7iUNoxotnm6hcR5jjBn5Yt5F+NPiBefQTfA7CIux3q5mIF2qM4evKWXdCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:37:58.015385Z","bundle_sha256":"0d1368e6b55b54b74621ba55537e9a21910f74382589b04bf9e6e1cf003367b7"}}