{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BHYMOGKA7WSQWK4BLDALMH6T6Z","short_pith_number":"pith:BHYMOGKA","canonical_record":{"source":{"id":"1904.11238","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-25T09:53:48Z","cross_cats_sorted":[],"title_canon_sha256":"92359f75397a38d4ef01bd6d96587b3dae3d33659295159225134df8be0f20af","abstract_canon_sha256":"f9e92db9e56b3ba94de10315e10d546162c228c7cea0724fa446fc0b332c0761"},"schema_version":"1.0"},"canonical_sha256":"09f0c71940fda50b2b8158c0b61fd3f67bb46cf370ebd992e43c1f66a2c24df4","source":{"kind":"arxiv","id":"1904.11238","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.11238","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1904.11238v2","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11238","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"BHYMOGKA7WSQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BHYMOGKA7WSQWK4B","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BHYMOGKA","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BHYMOGKA7WSQWK4BLDALMH6T6Z","target":"record","payload":{"canonical_record":{"source":{"id":"1904.11238","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-25T09:53:48Z","cross_cats_sorted":[],"title_canon_sha256":"92359f75397a38d4ef01bd6d96587b3dae3d33659295159225134df8be0f20af","abstract_canon_sha256":"f9e92db9e56b3ba94de10315e10d546162c228c7cea0724fa446fc0b332c0761"},"schema_version":"1.0"},"canonical_sha256":"09f0c71940fda50b2b8158c0b61fd3f67bb46cf370ebd992e43c1f66a2c24df4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:06.914724Z","signature_b64":"MN9doaztQXKLOn7nHPIxT0ReH/oMXy/zytkfxuy3N8JRlSnVJkARZk8sPEslhp2pcA6rxOBLx5ROi9TWSrxrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09f0c71940fda50b2b8158c0b61fd3f67bb46cf370ebd992e43c1f66a2c24df4","last_reissued_at":"2026-05-17T23:44:06.914260Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:06.914260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.11238","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lB3r30Vs8bsDYFMccrxR22PHI+TC/q7UiESf3/+tL6Cs9+6qlfPx5FuoYemnN8jszC8vc5o1b3BcqF9esDWIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:12:02.281729Z"},"content_sha256":"237282b1cb5c98dab0c3028a14dd7b7d11d406072308299f69d203e4ee207bd4","schema_version":"1.0","event_id":"sha256:237282b1cb5c98dab0c3028a14dd7b7d11d406072308299f69d203e4ee207bd4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BHYMOGKA7WSQWK4BLDALMH6T6Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Label Noise Modeling and Loss Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Diego Ortego, Eric Arazo, Kevin McGuinness, Noel E. O'Connor, Paul Albert","submitted_at":"2019-04-25T09:53:48Z","abstract_excerpt":"Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11238","kind":"arxiv","version":2},"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":"2026-05-17T23:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"djX311fslrcTy0czSyGkok7nuRplqNetGisqW3OeckEFDu72LLXE2jhcdmD+EBF4uNGZUwPqCidczzIDJHwaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:12:02.282384Z"},"content_sha256":"70a1f516f97ad3cd86ea582c9086072876850c6343fe45bee3f0f5f66bcab340","schema_version":"1.0","event_id":"sha256:70a1f516f97ad3cd86ea582c9086072876850c6343fe45bee3f0f5f66bcab340"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/bundle.json","state_url":"https://pith.science/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/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":"2026-06-01T21:12:02Z","links":{"resolver":"https://pith.science/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z","bundle":"https://pith.science/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/bundle.json","state":"https://pith.science/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BHYMOGKA7WSQWK4BLDALMH6T6Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BHYMOGKA7WSQWK4BLDALMH6T6Z","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":"f9e92db9e56b3ba94de10315e10d546162c228c7cea0724fa446fc0b332c0761","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-25T09:53:48Z","title_canon_sha256":"92359f75397a38d4ef01bd6d96587b3dae3d33659295159225134df8be0f20af"},"schema_version":"1.0","source":{"id":"1904.11238","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.11238","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1904.11238v2","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11238","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"BHYMOGKA7WSQ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BHYMOGKA7WSQWK4B","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BHYMOGKA","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:70a1f516f97ad3cd86ea582c9086072876850c6343fe45bee3f0f5f66bcab340","target":"graph","created_at":"2026-05-17T23:44:06Z","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":"Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the n","authors_text":"Diego Ortego, Eric Arazo, Kevin McGuinness, Noel E. O'Connor, Paul Albert","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-25T09:53:48Z","title":"Unsupervised Label Noise Modeling and Loss Correction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11238","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":"sha256:237282b1cb5c98dab0c3028a14dd7b7d11d406072308299f69d203e4ee207bd4","target":"record","created_at":"2026-05-17T23:44:06Z","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":"f9e92db9e56b3ba94de10315e10d546162c228c7cea0724fa446fc0b332c0761","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-25T09:53:48Z","title_canon_sha256":"92359f75397a38d4ef01bd6d96587b3dae3d33659295159225134df8be0f20af"},"schema_version":"1.0","source":{"id":"1904.11238","kind":"arxiv","version":2}},"canonical_sha256":"09f0c71940fda50b2b8158c0b61fd3f67bb46cf370ebd992e43c1f66a2c24df4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09f0c71940fda50b2b8158c0b61fd3f67bb46cf370ebd992e43c1f66a2c24df4","first_computed_at":"2026-05-17T23:44:06.914260Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:06.914260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MN9doaztQXKLOn7nHPIxT0ReH/oMXy/zytkfxuy3N8JRlSnVJkARZk8sPEslhp2pcA6rxOBLx5ROi9TWSrxrAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:06.914724Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.11238","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:237282b1cb5c98dab0c3028a14dd7b7d11d406072308299f69d203e4ee207bd4","sha256:70a1f516f97ad3cd86ea582c9086072876850c6343fe45bee3f0f5f66bcab340"],"state_sha256":"cc88876b2db5d329007cafcb1f7acb222b6a29b6a65691952880423e2f6eaeb8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+TzdV3ThVRw8jaSBUNHHyrafFZ5SUR7JdwaamOPROY1kEwAa5TVTtWwT2TuFV3S2Oszn0UBcqOKm9eFoXs82CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T21:12:02.285375Z","bundle_sha256":"05088c90a1a8bcc428d2955c1e0d6832b3005f345bf0b939665ea5787fc519a0"}}