{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:YYDUJY4TDMJGOVPU6WYAKSKIAM","short_pith_number":"pith:YYDUJY4T","canonical_record":{"source":{"id":"2204.10535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-22T06:58:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"00371355def4a6d16f208bd4f0e47cc6f537899fd8f0939aab2f7bf17f6db953","abstract_canon_sha256":"81e12e63aa605050a61d08dec8d733144d43d26114eaedf8048e854370dd216b"},"schema_version":"1.0"},"canonical_sha256":"c60744e3931b126755f4f5b0054948032b1dc03d6e2cf290c2e16b0aebd315c2","source":{"kind":"arxiv","id":"2204.10535","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.10535","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"arxiv_version","alias_value":"2204.10535v2","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.10535","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_12","alias_value":"YYDUJY4TDMJG","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_16","alias_value":"YYDUJY4TDMJGOVPU","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_8","alias_value":"YYDUJY4T","created_at":"2026-07-05T04:21:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:YYDUJY4TDMJGOVPU6WYAKSKIAM","target":"record","payload":{"canonical_record":{"source":{"id":"2204.10535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-22T06:58:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"00371355def4a6d16f208bd4f0e47cc6f537899fd8f0939aab2f7bf17f6db953","abstract_canon_sha256":"81e12e63aa605050a61d08dec8d733144d43d26114eaedf8048e854370dd216b"},"schema_version":"1.0"},"canonical_sha256":"c60744e3931b126755f4f5b0054948032b1dc03d6e2cf290c2e16b0aebd315c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:21:12.714568Z","signature_b64":"I4S0iOAAyaFpBq9qgUvkUvU2OvkbCJVB3LNfln4a4MJ4guXUQYNXR+xKGls5zzFN1YT7RwE22fiTU82QuAocBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c60744e3931b126755f4f5b0054948032b1dc03d6e2cf290c2e16b0aebd315c2","last_reissued_at":"2026-07-05T04:21:12.714163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:21:12.714163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.10535","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:21:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bdvkRYU7XmQVRwVGRlql0HVsLXLxEzA4AFbbaIopVjdatAgLRRiTEOfBiX85TONbFi3bycOKIlydwDDHzf+jAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:37:48.876794Z"},"content_sha256":"c2b5c63b3be9b76be4604e07582cc35b14064d22b4a13c2c5ca1dba185095308","schema_version":"1.0","event_id":"sha256:c2b5c63b3be9b76be4604e07582cc35b14064d22b4a13c2c5ca1dba185095308"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:YYDUJY4TDMJGOVPU6WYAKSKIAM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Alleviating Representational Shift for Continual Fine-tuning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shibo Jie, Zhi-Hong Deng, Ziheng Li","submitted_at":"2022-04-22T06:58:20Z","abstract_excerpt":"We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers' representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normaliz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.10535","kind":"arxiv","version":2},"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/2204.10535/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":"2026-07-05T04:21:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K67zkIvQgMk5ooypfTvIwM5vg4sTfbmCHRE6ctYoITKKVuGFKBnDdu0u9Gu5x0CDfX0oFWrEOVn0dZdeFfBnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:37:48.877338Z"},"content_sha256":"e86190e5b46c452089747afd23cf4254d292df60a70de5b77d3f0be4b92c469a","schema_version":"1.0","event_id":"sha256:e86190e5b46c452089747afd23cf4254d292df60a70de5b77d3f0be4b92c469a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/bundle.json","state_url":"https://pith.science/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/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":"2026-08-18T12:37:48Z","links":{"resolver":"https://pith.science/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM","bundle":"https://pith.science/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/bundle.json","state":"https://pith.science/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YYDUJY4TDMJGOVPU6WYAKSKIAM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:YYDUJY4TDMJGOVPU6WYAKSKIAM","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":"81e12e63aa605050a61d08dec8d733144d43d26114eaedf8048e854370dd216b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-22T06:58:20Z","title_canon_sha256":"00371355def4a6d16f208bd4f0e47cc6f537899fd8f0939aab2f7bf17f6db953"},"schema_version":"1.0","source":{"id":"2204.10535","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.10535","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"arxiv_version","alias_value":"2204.10535v2","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.10535","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_12","alias_value":"YYDUJY4TDMJG","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_16","alias_value":"YYDUJY4TDMJGOVPU","created_at":"2026-07-05T04:21:12Z"},{"alias_kind":"pith_short_8","alias_value":"YYDUJY4T","created_at":"2026-07-05T04:21:12Z"}],"graph_snapshots":[{"event_id":"sha256:e86190e5b46c452089747afd23cf4254d292df60a70de5b77d3f0be4b92c469a","target":"graph","created_at":"2026-07-05T04:21:12Z","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/2204.10535/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers' representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normaliz","authors_text":"Shibo Jie, Zhi-Hong Deng, Ziheng Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-22T06:58:20Z","title":"Alleviating Representational Shift for Continual Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.10535","kind":"arxiv","version":2},"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:c2b5c63b3be9b76be4604e07582cc35b14064d22b4a13c2c5ca1dba185095308","target":"record","created_at":"2026-07-05T04:21:12Z","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":"81e12e63aa605050a61d08dec8d733144d43d26114eaedf8048e854370dd216b","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-22T06:58:20Z","title_canon_sha256":"00371355def4a6d16f208bd4f0e47cc6f537899fd8f0939aab2f7bf17f6db953"},"schema_version":"1.0","source":{"id":"2204.10535","kind":"arxiv","version":2}},"canonical_sha256":"c60744e3931b126755f4f5b0054948032b1dc03d6e2cf290c2e16b0aebd315c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c60744e3931b126755f4f5b0054948032b1dc03d6e2cf290c2e16b0aebd315c2","first_computed_at":"2026-07-05T04:21:12.714163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:21:12.714163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I4S0iOAAyaFpBq9qgUvkUvU2OvkbCJVB3LNfln4a4MJ4guXUQYNXR+xKGls5zzFN1YT7RwE22fiTU82QuAocBA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:21:12.714568Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.10535","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c2b5c63b3be9b76be4604e07582cc35b14064d22b4a13c2c5ca1dba185095308","sha256:e86190e5b46c452089747afd23cf4254d292df60a70de5b77d3f0be4b92c469a"],"state_sha256":"4b0fa7a8835d2f0e4f63cba49e134bde18364bdfb828156f35be62b8221ddc76"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KituF6n1gqxTf05nTpRtWCGt+00KMahoLpnk+fZo1i+c8WPJRQvmGfhoZPjjXMknkOjMIElRFaAcwK6uMAYQDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:37:48.881282Z","bundle_sha256":"0b33e85b322c5f31bd413eba59ac6d33b3b30464221d8ea4448d382de9adc1d5"}}