{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KNNFE6STLWJLOULCFDYL36B3E7","short_pith_number":"pith:KNNFE6ST","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"},"canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","source":{"kind":"arxiv","id":"2404.12922","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.12922","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"arxiv_version","alias_value":"2404.12922v1","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.12922","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_12","alias_value":"KNNFE6STLWJL","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_16","alias_value":"KNNFE6STLWJLOULC","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_8","alias_value":"KNNFE6ST","created_at":"2026-07-05T08:10:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KNNFE6STLWJLOULCFDYL36B3E7","target":"record","payload":{"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"},"canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","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"},"source_kind":"arxiv","source_id":"2404.12922","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:10:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FwaG0IBW8NyxXlFEcjp+FOSr/dCC3qR4OqvG3aw5luVuKkk0zTt2jCU686c9hb5sRPF9JOp5OchD/4e2RBQBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:59:20.685319Z"},"content_sha256":"0502f51c45b770401b8d6c9932d680451cc8296abbc2ad606c6a10356df20e29","schema_version":"1.0","event_id":"sha256:0502f51c45b770401b8d6c9932d680451cc8296abbc2ad606c6a10356df20e29"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KNNFE6STLWJLOULCFDYL36B3E7","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:10:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dhw3akK1DUkuTyJbCTQW4yXmPoqgxA1CGdlTbdDhgju6ffdyS3Ls7pga0/hftRxkegupvX5IzDinLiSZEkLABg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:59:20.686116Z"},"content_sha256":"d5aa7682c34baf7eccfb320bd7a7e6c26d1d8336bc44170660c16a1334bc9548","schema_version":"1.0","event_id":"sha256:d5aa7682c34baf7eccfb320bd7a7e6c26d1d8336bc44170660c16a1334bc9548"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/bundle.json","state_url":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KNNFE6STLWJLOULCFDYL36B3E7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T06:59:20Z","links":{"resolver":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7","bundle":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/bundle.json","state":"https://pith.science/pith/KNNFE6STLWJLOULCFDYL36B3E7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KNNFE6STLWJLOULCFDYL36B3E7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KNNFE6STLWJLOULCFDYL36B3E7","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":"52103fa76d407aea88bb9b799fd0ad9b196273c8a68d4c910e4db8791248a1de","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-19T14:45:27Z","title_canon_sha256":"c29f01b742f492c156cb3cbcb599eb34674e51953b80d9e5b8466ba2524d563d"},"schema_version":"1.0","source":{"id":"2404.12922","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.12922","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"arxiv_version","alias_value":"2404.12922v1","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.12922","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_12","alias_value":"KNNFE6STLWJL","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_16","alias_value":"KNNFE6STLWJLOULC","created_at":"2026-07-05T08:10:00Z"},{"alias_kind":"pith_short_8","alias_value":"KNNFE6ST","created_at":"2026-07-05T08:10:00Z"}],"graph_snapshots":[{"event_id":"sha256:d5aa7682c34baf7eccfb320bd7a7e6c26d1d8336bc44170660c16a1334bc9548","target":"graph","created_at":"2026-07-05T08:10:00Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2404.12922/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Jacopo Bonato, Luigi Sabetta, Marco Cotogni","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-19T14:45:27Z","title":"Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.12922","kind":"arxiv","version":1},"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":"sha256:0502f51c45b770401b8d6c9932d680451cc8296abbc2ad606c6a10356df20e29","target":"record","created_at":"2026-07-05T08:10:00Z","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":"52103fa76d407aea88bb9b799fd0ad9b196273c8a68d4c910e4db8791248a1de","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-19T14:45:27Z","title_canon_sha256":"c29f01b742f492c156cb3cbcb599eb34674e51953b80d9e5b8466ba2524d563d"},"schema_version":"1.0","source":{"id":"2404.12922","kind":"arxiv","version":1}},"canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"535a527a535d92b7516228f0bdf83b27c246a025d79de47e178cfaf16c8f36af","first_computed_at":"2026-07-05T08:10:00.839820Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:10:00.839820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wNtvUKUBALpUPYTXK2gUunIFJ3qcyHBziHtJa+PD1w/nVBtAO1TNLJdz5363zyvWSS+Un+3vmo0NlGHSJEfhBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:10:00.840165Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.12922","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0502f51c45b770401b8d6c9932d680451cc8296abbc2ad606c6a10356df20e29","sha256:d5aa7682c34baf7eccfb320bd7a7e6c26d1d8336bc44170660c16a1334bc9548"],"state_sha256":"3d2fa102089be07b249b2d2ec6c3d74392db36e8b0478b08654e3e40189ac750"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JgpJNibjXEcMYiKqE6ORV58nsApAfMSSoJQYhcM0AVMKQIqaAbEsXS7D8uXN6w3BRiyhM0xlUQGfArFFDXqoCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T06:59:20.689474Z","bundle_sha256":"3a2a45260301b1013bed3b28a0382f6d0e905970833723f52c377b8c2f21ef5d"}}