{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P62HU4TDSBCENIHV3I2TV7JKJU","short_pith_number":"pith:P62HU4TD","schema_version":"1.0","canonical_sha256":"7fb47a7263904446a0f5da353afd2a4d0d4638bbf1894592ab8886fb368a84f3","source":{"kind":"arxiv","id":"2308.13320","version":3},"attestation_state":"computed","paper":{"title":"Fine-tuning can cripple your foundation model; preserving features may be the solution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jishnu Mukhoti, Philip H.S. Torr, Puneet K. Dokania, Yarin Gal","submitted_at":"2023-08-25T11:49:51Z","abstract_excerpt":"Pre-trained foundation models, due to their enormous capacity and exposure to vast amounts of data during pre-training, are known to have learned plenty of real-world concepts. An important step in making these pre-trained models effective on downstream tasks is to fine-tune them on related datasets. While various fine-tuning methods have been devised and have been shown to be highly effective, we observe that a fine-tuned model's ability to recognize concepts on tasks $\\textit{different}$ from the downstream one is reduced significantly compared to its pre-trained counterpart. This is an unde"},"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":"2308.13320","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-25T11:49:51Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a37cc7e790f845e8b28236b49ccb1da0a562c7d3691ce0bd3dc822b822887c3e","abstract_canon_sha256":"c8d6ec8a23aee71add7dc33a1a19d1bc9862b98be3893bc90d760cc8c0cae0a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:34.138074Z","signature_b64":"PWzvmLZR8a3anG14xGG4Lccft0ym+ZtJp9fd4bkfxQVvMqQFOLoSAeqzVuDh/WZjucD7YU3wW/wuVIxZFWYlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7fb47a7263904446a0f5da353afd2a4d0d4638bbf1894592ab8886fb368a84f3","last_reissued_at":"2026-07-05T08:38:34.137542Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:34.137542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fine-tuning can cripple your foundation model; preserving features may be the solution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jishnu Mukhoti, Philip H.S. Torr, Puneet K. Dokania, Yarin Gal","submitted_at":"2023-08-25T11:49:51Z","abstract_excerpt":"Pre-trained foundation models, due to their enormous capacity and exposure to vast amounts of data during pre-training, are known to have learned plenty of real-world concepts. An important step in making these pre-trained models effective on downstream tasks is to fine-tune them on related datasets. While various fine-tuning methods have been devised and have been shown to be highly effective, we observe that a fine-tuned model's ability to recognize concepts on tasks $\\textit{different}$ from the downstream one is reduced significantly compared to its pre-trained counterpart. This is an unde"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13320","kind":"arxiv","version":3},"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/2308.13320/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":"2308.13320","created_at":"2026-07-05T08:38:34.137611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13320v3","created_at":"2026-07-05T08:38:34.137611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13320","created_at":"2026-07-05T08:38:34.137611+00:00"},{"alias_kind":"pith_short_12","alias_value":"P62HU4TDSBCE","created_at":"2026-07-05T08:38:34.137611+00:00"},{"alias_kind":"pith_short_16","alias_value":"P62HU4TDSBCENIHV","created_at":"2026-07-05T08:38:34.137611+00:00"},{"alias_kind":"pith_short_8","alias_value":"P62HU4TD","created_at":"2026-07-05T08:38:34.137611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20110","citing_title":"FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07970","citing_title":"Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06667","citing_title":"The Piggyback Hypothesis of Generalization: Explaining and Mitigating Emergent Misalignment","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2409.18169","citing_title":"Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15961","citing_title":"Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12384","citing_title":"Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU","json":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU.json","graph_json":"https://pith.science/api/pith-number/P62HU4TDSBCENIHV3I2TV7JKJU/graph.json","events_json":"https://pith.science/api/pith-number/P62HU4TDSBCENIHV3I2TV7JKJU/events.json","paper":"https://pith.science/paper/P62HU4TD"},"agent_actions":{"view_html":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU","download_json":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU.json","view_paper":"https://pith.science/paper/P62HU4TD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13320&json=true","fetch_graph":"https://pith.science/api/pith-number/P62HU4TDSBCENIHV3I2TV7JKJU/graph.json","fetch_events":"https://pith.science/api/pith-number/P62HU4TDSBCENIHV3I2TV7JKJU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU/action/storage_attestation","attest_author":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU/action/author_attestation","sign_citation":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU/action/citation_signature","submit_replication":"https://pith.science/pith/P62HU4TDSBCENIHV3I2TV7JKJU/action/replication_record"}},"created_at":"2026-07-05T08:38:34.137611+00:00","updated_at":"2026-07-05T08:38:34.137611+00:00"}