{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RUMYFKSLNJNDK2XP64TMKKJCAV","short_pith_number":"pith:RUMYFKSL","schema_version":"1.0","canonical_sha256":"8d1982aa4b6a5a356aeff726c529220555742c6edb01fe683d68aadd3b2bb2b1","source":{"kind":"arxiv","id":"2412.01705","version":1},"attestation_state":"computed","paper":{"title":"Uncertainty-Aware Regularization for Image-to-Image Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Anuja Vats, Ivar Farup, Kiran Raja, Marius Pedersen","submitted_at":"2024-11-24T14:05:27Z","abstract_excerpt":"The importance of quantifying uncertainty in deep networks has become paramount for reliable real-world applications. In this paper, we propose a method to improve uncertainty estimation in medical Image-to-Image (I2I) translation. Our model integrates aleatoric uncertainty and employs Uncertainty-Aware Regularization (UAR) inspired by simple priors to refine uncertainty estimates and enhance reconstruction quality. We show that by leveraging simple priors on parameters, our approach captures more robust uncertainty maps, effectively refining them to indicate precisely where the network encoun"},"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":"2412.01705","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-24T14:05:27Z","cross_cats_sorted":["cs.AI","eess.IV"],"title_canon_sha256":"ad981cabc9ab8c2a43d7cb1a082caa0f273129ebeaa1aee7dcb511719f723bdf","abstract_canon_sha256":"a0727f1b5977dcd0830e56c3aa5cf00d766038482b11089f124bf2b65c3cbc54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:17.657120Z","signature_b64":"Q0/pQaBqryJdqt7eHsC9FIK6TN6p5CYu+WbfqJ5AZbcFhxAnJgoo21wVmI1qEM7aZXwi2JDOQuxR4YvHcsSYDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d1982aa4b6a5a356aeff726c529220555742c6edb01fe683d68aadd3b2bb2b1","last_reissued_at":"2026-07-05T09:43:17.656608Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:17.656608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncertainty-Aware Regularization for Image-to-Image Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Anuja Vats, Ivar Farup, Kiran Raja, Marius Pedersen","submitted_at":"2024-11-24T14:05:27Z","abstract_excerpt":"The importance of quantifying uncertainty in deep networks has become paramount for reliable real-world applications. In this paper, we propose a method to improve uncertainty estimation in medical Image-to-Image (I2I) translation. Our model integrates aleatoric uncertainty and employs Uncertainty-Aware Regularization (UAR) inspired by simple priors to refine uncertainty estimates and enhance reconstruction quality. We show that by leveraging simple priors on parameters, our approach captures more robust uncertainty maps, effectively refining them to indicate precisely where the network encoun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01705","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/2412.01705/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":"2412.01705","created_at":"2026-07-05T09:43:17.656660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.01705v1","created_at":"2026-07-05T09:43:17.656660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01705","created_at":"2026-07-05T09:43:17.656660+00:00"},{"alias_kind":"pith_short_12","alias_value":"RUMYFKSLNJND","created_at":"2026-07-05T09:43:17.656660+00:00"},{"alias_kind":"pith_short_16","alias_value":"RUMYFKSLNJNDK2XP","created_at":"2026-07-05T09:43:17.656660+00:00"},{"alias_kind":"pith_short_8","alias_value":"RUMYFKSL","created_at":"2026-07-05T09:43:17.656660+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/RUMYFKSLNJNDK2XP64TMKKJCAV","json":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV.json","graph_json":"https://pith.science/api/pith-number/RUMYFKSLNJNDK2XP64TMKKJCAV/graph.json","events_json":"https://pith.science/api/pith-number/RUMYFKSLNJNDK2XP64TMKKJCAV/events.json","paper":"https://pith.science/paper/RUMYFKSL"},"agent_actions":{"view_html":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV","download_json":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV.json","view_paper":"https://pith.science/paper/RUMYFKSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.01705&json=true","fetch_graph":"https://pith.science/api/pith-number/RUMYFKSLNJNDK2XP64TMKKJCAV/graph.json","fetch_events":"https://pith.science/api/pith-number/RUMYFKSLNJNDK2XP64TMKKJCAV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV/action/storage_attestation","attest_author":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV/action/author_attestation","sign_citation":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV/action/citation_signature","submit_replication":"https://pith.science/pith/RUMYFKSLNJNDK2XP64TMKKJCAV/action/replication_record"}},"created_at":"2026-07-05T09:43:17.656660+00:00","updated_at":"2026-07-05T09:43:17.656660+00:00"}