{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ARSN5OS7YWXGCX4XNMO2DFEF6V","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":"c13adc9251ca21a76363514a315de578cea8bdab8d7bae84115a780298ad355f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-31T18:16:49Z","title_canon_sha256":"463a25d6058671f1a62e0a6f3489c449d9cfec31231775f13920e72bcf50b6dd"},"schema_version":"1.0","source":{"id":"1706.00038","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00038","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00038v2","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00038","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"pith_short_12","alias_value":"ARSN5OS7YWXG","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"ARSN5OS7YWXGCX4X","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"ARSN5OS7","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:feec1d39a606e1d4b8873ce48f5a0bc396dc7371384be1e2c4e5858010178af2","target":"graph","created_at":"2026-05-18T00:31:28Z","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":"Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. This paper proposes a novel framework for training deep convolutional neural networks from noisy labeled datasets that can be obtained cheaply. The problem is formulated using an undirected graphical model that represents the relationship between noisy and clean labels, trained in a semi-supervised setting. In our formulation, the inference over latent clean labels is tractable and is regularized during training using auxiliary sources of information. The proposed model is applied to the image","authors_text":"Arash Vahdat","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-31T18:16:49Z","title":"Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00038","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":"sha256:2366ccf583c0f763aab744d5258e2b529f9f7a9919658f28f3b4f86b0ba8944f","target":"record","created_at":"2026-05-18T00:31:28Z","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":"c13adc9251ca21a76363514a315de578cea8bdab8d7bae84115a780298ad355f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-31T18:16:49Z","title_canon_sha256":"463a25d6058671f1a62e0a6f3489c449d9cfec31231775f13920e72bcf50b6dd"},"schema_version":"1.0","source":{"id":"1706.00038","kind":"arxiv","version":2}},"canonical_sha256":"0464deba5fc5ae615f976b1da19485f5439cedf3689461378802ed7d6af08517","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0464deba5fc5ae615f976b1da19485f5439cedf3689461378802ed7d6af08517","first_computed_at":"2026-05-18T00:31:28.822847Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:28.822847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hDZVOyDttLnO2Z3tBrsVsBHcp1LULQUP7op2b93prRRNyg2DznrXxE+gkLVjI8EmGd7bmu8tauNJL/8X5aohBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:28.823618Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.00038","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2366ccf583c0f763aab744d5258e2b529f9f7a9919658f28f3b4f86b0ba8944f","sha256:feec1d39a606e1d4b8873ce48f5a0bc396dc7371384be1e2c4e5858010178af2"],"state_sha256":"a036508edc08421f04a8d4037db4a511e3e8e99bf92a56bdb1d57613e23d60b3"}