{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TDPIZIOF35WMJCYWY4TAEBNBHZ","short_pith_number":"pith:TDPIZIOF","schema_version":"1.0","canonical_sha256":"98de8ca1c5df6cc48b16c7260205a13e7833634d454cca6ceb1760e76f5c2420","source":{"kind":"arxiv","id":"2305.17303","version":7},"attestation_state":"computed","paper":{"title":"Distilling BlackBox to Interpretable models for Efficient Transfer Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Kayhan Batmanghelich, Ke Yu, Shantanu Ghosh","submitted_at":"2023-05-26T23:23:48Z","abstract_excerpt":"Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be"},"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":"2305.17303","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-26T23:23:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"087209f7d0345f22ee486b441da8f586bdd8201367af381676c7d22f3934948b","abstract_canon_sha256":"cde91cc68b48b2420df9bd7e9e5fb26dbc417ad56d15c9918cfecbd9ed8aa660"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:29:51.461851Z","signature_b64":"PqpSmk3lUvHeY7urwJj2UyDsxP2Qg/9RABwHeFHB6mGMU8feoUfIIW4LZqg3opDKUsKH8ME6MmJe8sCP74r9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98de8ca1c5df6cc48b16c7260205a13e7833634d454cca6ceb1760e76f5c2420","last_reissued_at":"2026-07-05T06:29:51.461328Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:29:51.461328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distilling BlackBox to Interpretable models for Efficient Transfer Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Kayhan Batmanghelich, Ke Yu, Shantanu Ghosh","submitted_at":"2023-05-26T23:23:48Z","abstract_excerpt":"Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17303","kind":"arxiv","version":7},"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/2305.17303/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":"2305.17303","created_at":"2026-07-05T06:29:51.461389+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.17303v7","created_at":"2026-07-05T06:29:51.461389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17303","created_at":"2026-07-05T06:29:51.461389+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDPIZIOF35WM","created_at":"2026-07-05T06:29:51.461389+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDPIZIOF35WMJCYW","created_at":"2026-07-05T06:29:51.461389+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDPIZIOF","created_at":"2026-07-05T06:29:51.461389+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.05545","citing_title":"PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ","json":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ.json","graph_json":"https://pith.science/api/pith-number/TDPIZIOF35WMJCYWY4TAEBNBHZ/graph.json","events_json":"https://pith.science/api/pith-number/TDPIZIOF35WMJCYWY4TAEBNBHZ/events.json","paper":"https://pith.science/paper/TDPIZIOF"},"agent_actions":{"view_html":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ","download_json":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ.json","view_paper":"https://pith.science/paper/TDPIZIOF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.17303&json=true","fetch_graph":"https://pith.science/api/pith-number/TDPIZIOF35WMJCYWY4TAEBNBHZ/graph.json","fetch_events":"https://pith.science/api/pith-number/TDPIZIOF35WMJCYWY4TAEBNBHZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ/action/storage_attestation","attest_author":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ/action/author_attestation","sign_citation":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ/action/citation_signature","submit_replication":"https://pith.science/pith/TDPIZIOF35WMJCYWY4TAEBNBHZ/action/replication_record"}},"created_at":"2026-07-05T06:29:51.461389+00:00","updated_at":"2026-07-05T06:29:51.461389+00:00"}