{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KNNFE6STLWJLOULCFDYL36B3E7","short_pith_number":"pith:KNNFE6ST","schema_version":"1.0","canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","source":{"kind":"arxiv","id":"2404.12922","version":1},"attestation_state":"computed","paper":{"title":"Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jacopo Bonato, Luigi Sabetta, Marco Cotogni","submitted_at":"2024-04-19T14:45:27Z","abstract_excerpt":"In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using a retain set, which is a key component in state-of-the-art approximate unlearning algorithms. Our approach utilizes a modified Mahalanobis distance to guide the unlearning of the feature vectors of the instances to be forgotten, aligning them to the nearest wrong class distribution. Moreover, we propose a distillation-trick mechanism that distills"},"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":"2404.12922","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-19T14:45:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c29f01b742f492c156cb3cbcb599eb34674e51953b80d9e5b8466ba2524d563d","abstract_canon_sha256":"52103fa76d407aea88bb9b799fd0ad9b196273c8a68d4c910e4db8791248a1de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:10:00.840165Z","signature_b64":"wNtvUKUBALpUPYTXK2gUunIFJ3qcyHBziHtJa+PD1w/nVBtAO1TNLJdz5363zyvWSS+Un+3vmo0NlGHSJEfhBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","last_reissued_at":"2026-07-05T08:10:00.839820Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:10:00.839820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jacopo Bonato, Luigi Sabetta, Marco Cotogni","submitted_at":"2024-04-19T14:45:27Z","abstract_excerpt":"In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using a retain set, which is a key component in state-of-the-art approximate unlearning algorithms. Our approach utilizes a modified Mahalanobis distance to guide the unlearning of the feature vectors of the instances to be forgotten, aligning them to the nearest wrong class distribution. Moreover, we propose a distillation-trick mechanism that distills"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.12922","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/2404.12922/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":"2404.12922","created_at":"2026-07-05T08:10:00.839875+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.12922v1","created_at":"2026-07-05T08:10:00.839875+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.12922","created_at":"2026-07-05T08:10:00.839875+00:00"},{"alias_kind":"pith_short_12","alias_value":"KNNFE6STLWJL","created_at":"2026-07-05T08:10:00.839875+00:00"},{"alias_kind":"pith_short_16","alias_value":"KNNFE6STLWJLOULC","created_at":"2026-07-05T08:10:00.839875+00:00"},{"alias_kind":"pith_short_8","alias_value":"KNNFE6ST","created_at":"2026-07-05T08:10:00.839875+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06073","citing_title":"System-Aware Unlearning Algorithms: Use Lesser, Forget Faster","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7","json":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7.json","graph_json":"https://pith.science/api/pith-number/KNNFE6STLWJLOULCFDYL36B3E7/graph.json","events_json":"https://pith.science/api/pith-number/KNNFE6STLWJLOULCFDYL36B3E7/events.json","paper":"https://pith.science/paper/KNNFE6ST"},"agent_actions":{"view_html":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7","download_json":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7.json","view_paper":"https://pith.science/paper/KNNFE6ST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.12922&json=true","fetch_graph":"https://pith.science/api/pith-number/KNNFE6STLWJLOULCFDYL36B3E7/graph.json","fetch_events":"https://pith.science/api/pith-number/KNNFE6STLWJLOULCFDYL36B3E7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/action/storage_attestation","attest_author":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/action/author_attestation","sign_citation":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/action/citation_signature","submit_replication":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/action/replication_record"}},"created_at":"2026-07-05T08:10:00.839875+00:00","updated_at":"2026-07-05T08:10:00.839875+00:00"}