{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5L3K5ABQLW6GH27CNIDDQ6X5TL","short_pith_number":"pith:5L3K5ABQ","schema_version":"1.0","canonical_sha256":"eaf6ae80305dbc63ebe26a06387afd9ae32bf6ce137bd9ee1fa9c7d061a2aded","source":{"kind":"arxiv","id":"2302.05142","version":1},"attestation_state":"computed","paper":{"title":"DOMINO: Domain-aware Loss for Deep Learning Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Adam J. Woods, Alejandro Albizu, Aprinda Indahlastari, Kevin Brink, Kyle Volle, Matthew Hale, Ruogu Fang, Skylar E. Stolte","submitted_at":"2023-02-10T09:47:46Z","abstract_excerpt":"Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even w"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2302.05142","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-10T09:47:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"aac12047847d65abd8232440d5e8ccc2b930b100e586bd0951a45db5ee493a05","abstract_canon_sha256":"981914a28edc96992970d012b419757c0b9ecf66e2f18b76e1188d553b6b7a75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:36.019240Z","signature_b64":"s6v+KTL0T3ToIG3HIWSXQE8Z/N9Su0Eo6FRn2VQZJxeyJeEA7Xl4lQQh9BIPm0UJ/Z8dzcYW9qgb/lorDPNOAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eaf6ae80305dbc63ebe26a06387afd9ae32bf6ce137bd9ee1fa9c7d061a2aded","last_reissued_at":"2026-07-05T05:40:36.018796Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:36.018796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DOMINO: Domain-aware Loss for Deep Learning Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Adam J. Woods, Alejandro Albizu, Aprinda Indahlastari, Kevin Brink, Kyle Volle, Matthew Hale, Ruogu Fang, Skylar E. Stolte","submitted_at":"2023-02-10T09:47:46Z","abstract_excerpt":"Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05142","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/2302.05142/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2302.05142","created_at":"2026-07-05T05:40:36.018856+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.05142v1","created_at":"2026-07-05T05:40:36.018856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05142","created_at":"2026-07-05T05:40:36.018856+00:00"},{"alias_kind":"pith_short_12","alias_value":"5L3K5ABQLW6G","created_at":"2026-07-05T05:40:36.018856+00:00"},{"alias_kind":"pith_short_16","alias_value":"5L3K5ABQLW6GH27C","created_at":"2026-07-05T05:40:36.018856+00:00"},{"alias_kind":"pith_short_8","alias_value":"5L3K5ABQ","created_at":"2026-07-05T05:40:36.018856+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL","json":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL.json","graph_json":"https://pith.science/api/pith-number/5L3K5ABQLW6GH27CNIDDQ6X5TL/graph.json","events_json":"https://pith.science/api/pith-number/5L3K5ABQLW6GH27CNIDDQ6X5TL/events.json","paper":"https://pith.science/paper/5L3K5ABQ"},"agent_actions":{"view_html":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL","download_json":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL.json","view_paper":"https://pith.science/paper/5L3K5ABQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.05142&json=true","fetch_graph":"https://pith.science/api/pith-number/5L3K5ABQLW6GH27CNIDDQ6X5TL/graph.json","fetch_events":"https://pith.science/api/pith-number/5L3K5ABQLW6GH27CNIDDQ6X5TL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL/action/storage_attestation","attest_author":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL/action/author_attestation","sign_citation":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL/action/citation_signature","submit_replication":"https://pith.science/pith/5L3K5ABQLW6GH27CNIDDQ6X5TL/action/replication_record"}},"created_at":"2026-07-05T05:40:36.018856+00:00","updated_at":"2026-07-05T05:40:36.018856+00:00"}