{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:RVMGJEVFMXK2B2O7X7C3YJAHAO","short_pith_number":"pith:RVMGJEVF","canonical_record":{"source":{"id":"2011.05704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T11:15:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a209457d909b52f59b22eb6fa31aca16df34a31308c037b4c0291b2ae68a9aad","abstract_canon_sha256":"de4b682f10f9cb59deea00d715abe33b98be82c57b2c5f2757b6347679a58141"},"schema_version":"1.0"},"canonical_sha256":"8d586492a565d5a0e9dfbfc5bc2407038af616b94549a96d598cf2531922d016","source":{"kind":"arxiv","id":"2011.05704","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05704","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05704v1","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05704","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_12","alias_value":"RVMGJEVFMXK2","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_16","alias_value":"RVMGJEVFMXK2B2O7","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_8","alias_value":"RVMGJEVF","created_at":"2026-07-05T01:51:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:RVMGJEVFMXK2B2O7X7C3YJAHAO","target":"record","payload":{"canonical_record":{"source":{"id":"2011.05704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T11:15:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a209457d909b52f59b22eb6fa31aca16df34a31308c037b4c0291b2ae68a9aad","abstract_canon_sha256":"de4b682f10f9cb59deea00d715abe33b98be82c57b2c5f2757b6347679a58141"},"schema_version":"1.0"},"canonical_sha256":"8d586492a565d5a0e9dfbfc5bc2407038af616b94549a96d598cf2531922d016","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:51:00.816617Z","signature_b64":"amHyjiErjDjwMpFCRFoVjx1nXFQCWXbxzXijLGvXi543EhdP+d/oGYJ0x075qWpqG87ojFIrO1w14Y3F5GuwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d586492a565d5a0e9dfbfc5bc2407038af616b94549a96d598cf2531922d016","last_reissued_at":"2026-07-05T01:51:00.816241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:51:00.816241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.05704","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":"2026-07-05T01:51:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VV3ZjIIUy6wYDuyLZdEkJoF0aRliVDH+6bEhyjznKWLqXgD6vRBHHx14kzC5EUC+aKyUjjmaGv0NYFjjeKoBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T15:29:39.630939Z"},"content_sha256":"bf685e988e7ab63eaa96d65ddd8aa6576f567f363cef0a77b4c92a029646d20c","schema_version":"1.0","event_id":"sha256:bf685e988e7ab63eaa96d65ddd8aa6576f567f363cef0a77b4c92a029646d20c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:RVMGJEVFMXK2B2O7X7C3YJAHAO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Filipe R. Cordeiro, Gustavo Carneiro, Ian Reid, Ragav Sachdeva, Vasileios Belagiannis","submitted_at":"2020-11-11T11:15:32Z","abstract_excerpt":"The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. The field has addressed this problem by focusing on training models under two types of label noise: 1) closed-set noise, where some training samples are incorrectly annotated to a training label other than their known true class; and 2) open-set noise, where the tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05704","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.05704/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":"2026-07-05T01:51:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"guvzRq+dk/C/uEhW9iBYyJmx4BNBnuFhXf2BFfTZx+Lk6XT/jTTrrOCD9j+kNBv6tAg9QnlN6aOuOBIi4kWiDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T15:29:39.631447Z"},"content_sha256":"074a6e25a5580e5da4dd04bdf0c1b5138a5690fcf817d2845263c7e8708138c5","schema_version":"1.0","event_id":"sha256:074a6e25a5580e5da4dd04bdf0c1b5138a5690fcf817d2845263c7e8708138c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/bundle.json","state_url":"https://pith.science/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/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":"2026-08-18T15:29:39Z","links":{"resolver":"https://pith.science/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO","bundle":"https://pith.science/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/bundle.json","state":"https://pith.science/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RVMGJEVFMXK2B2O7X7C3YJAHAO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:RVMGJEVFMXK2B2O7X7C3YJAHAO","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":"de4b682f10f9cb59deea00d715abe33b98be82c57b2c5f2757b6347679a58141","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T11:15:32Z","title_canon_sha256":"a209457d909b52f59b22eb6fa31aca16df34a31308c037b4c0291b2ae68a9aad"},"schema_version":"1.0","source":{"id":"2011.05704","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.05704","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"arxiv_version","alias_value":"2011.05704v1","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05704","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_12","alias_value":"RVMGJEVFMXK2","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_16","alias_value":"RVMGJEVFMXK2B2O7","created_at":"2026-07-05T01:51:00Z"},{"alias_kind":"pith_short_8","alias_value":"RVMGJEVF","created_at":"2026-07-05T01:51:00Z"}],"graph_snapshots":[{"event_id":"sha256:074a6e25a5580e5da4dd04bdf0c1b5138a5690fcf817d2845263c7e8708138c5","target":"graph","created_at":"2026-07-05T01:51:00Z","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/2011.05704/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. The field has addressed this problem by focusing on training models under two types of label noise: 1) closed-set noise, where some training samples are incorrectly annotated to a training label other than their known true class; and 2) open-set noise, where the tr","authors_text":"Filipe R. Cordeiro, Gustavo Carneiro, Ian Reid, Ragav Sachdeva, Vasileios Belagiannis","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T11:15:32Z","title":"EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05704","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:bf685e988e7ab63eaa96d65ddd8aa6576f567f363cef0a77b4c92a029646d20c","target":"record","created_at":"2026-07-05T01:51:00Z","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":"de4b682f10f9cb59deea00d715abe33b98be82c57b2c5f2757b6347679a58141","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T11:15:32Z","title_canon_sha256":"a209457d909b52f59b22eb6fa31aca16df34a31308c037b4c0291b2ae68a9aad"},"schema_version":"1.0","source":{"id":"2011.05704","kind":"arxiv","version":1}},"canonical_sha256":"8d586492a565d5a0e9dfbfc5bc2407038af616b94549a96d598cf2531922d016","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d586492a565d5a0e9dfbfc5bc2407038af616b94549a96d598cf2531922d016","first_computed_at":"2026-07-05T01:51:00.816241Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:51:00.816241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"amHyjiErjDjwMpFCRFoVjx1nXFQCWXbxzXijLGvXi543EhdP+d/oGYJ0x075qWpqG87ojFIrO1w14Y3F5GuwAg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:51:00.816617Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.05704","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf685e988e7ab63eaa96d65ddd8aa6576f567f363cef0a77b4c92a029646d20c","sha256:074a6e25a5580e5da4dd04bdf0c1b5138a5690fcf817d2845263c7e8708138c5"],"state_sha256":"fb9a04d43bc4136571afa3295d39c2fcb1c206dc7cf1264f22cdc147ea095a1b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OkVIP4zSp0eKN51pctx3jtB7Di+8Pgy0vvSiwubYvkrkRMa8Li2pwVShsO062/CzKtjbDdCVwmKlY4J8q8a/Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T15:29:39.635201Z","bundle_sha256":"093be7fc2ab01d0f94045167331e315a060a207f5c4519f2a032676e1f57f701"}}