{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZOXFXTMVOXOU4OYLS6MXL6VUDQ","short_pith_number":"pith:ZOXFXTMV","canonical_record":{"source":{"id":"2204.03410","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-07T12:49:14Z","cross_cats_sorted":[],"title_canon_sha256":"6e55b53c601a8cf3a5f89270d3bf11bb7cae5cd81ac902b620b6a811d1bdb543","abstract_canon_sha256":"b2c3def96ca7629ace530bdbc66fc962fc971c97f5ce8e935150f30b3db1782b"},"schema_version":"1.0"},"canonical_sha256":"cbae5bcd9575dd4e3b0b979975fab41c2d091df58e3303894056def61d8e7b78","source":{"kind":"arxiv","id":"2204.03410","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.03410","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"arxiv_version","alias_value":"2204.03410v7","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.03410","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZOXFXTMVOXOU","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_16","alias_value":"ZOXFXTMVOXOU4OYL","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_8","alias_value":"ZOXFXTMV","created_at":"2026-07-05T05:40:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZOXFXTMVOXOU4OYLS6MXL6VUDQ","target":"record","payload":{"canonical_record":{"source":{"id":"2204.03410","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-07T12:49:14Z","cross_cats_sorted":[],"title_canon_sha256":"6e55b53c601a8cf3a5f89270d3bf11bb7cae5cd81ac902b620b6a811d1bdb543","abstract_canon_sha256":"b2c3def96ca7629ace530bdbc66fc962fc971c97f5ce8e935150f30b3db1782b"},"schema_version":"1.0"},"canonical_sha256":"cbae5bcd9575dd4e3b0b979975fab41c2d091df58e3303894056def61d8e7b78","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:05.271086Z","signature_b64":"ojCZZJbN3FUtkGs6yMWG8TTZBiv5pWOO80m/84z1BNsT5BMwIXnqo0h1PWilp2smDXfK98bTPB7Ok28FyvVKAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbae5bcd9575dd4e3b0b979975fab41c2d091df58e3303894056def61d8e7b78","last_reissued_at":"2026-07-05T05:40:05.270565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:05.270565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.03410","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:40:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"psrEB9z0edVWVXCKmX5muLD9aFuWDACrIiIK5Z6qd9roMWhH6sDCyhKDSD1pG3BEEl6TpajdxX6fF5YSVZpjAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:32:06.664783Z"},"content_sha256":"ec67fa5e5511eb48cf6846501d322020de6d3f7f5d1fe8d7a0227b19b1b346ba","schema_version":"1.0","event_id":"sha256:ec67fa5e5511eb48cf6846501d322020de6d3f7f5d1fe8d7a0227b19b1b346ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZOXFXTMVOXOU4OYLS6MXL6VUDQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Incremental Prototype Tuning for Class Incremental Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haojian Zhang, Jianhua Hu, Jieren Deng, Yunkuan Wang","submitted_at":"2022-04-07T12:49:14Z","abstract_excerpt":"Class incremental learning(CIL) has attracted much attention, but most existing related works focus on fine-tuning the entire representation model, which inevitably results in much catastrophic forgetting. In the contrast, with a semantic-rich pre-trained representation model, parameter-additional-tuning (PAT) only changes very few parameters to learn new visual concepts. Recent studies have proved that PAT-based CIL can naturally avoid fighting against forgetting by replaying or distilling like most of the existing methods. However, we find that PAT-based CIL still faces serious semantic drif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.03410","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/2204.03410/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":"2026-07-05T05:40:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VZ9kvwCXtqzV70/GhwJWhEu56yjCw0fZhULiXGulSUuzSGlwiRUoyvcNnfZ80hWdGeNiKnJHfoOX6mQcwVBtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:32:06.665155Z"},"content_sha256":"146c747a7fb4436cacf82d5b76024f0a9f1fa0de9d2dd7656d1303e0162132a3","schema_version":"1.0","event_id":"sha256:146c747a7fb4436cacf82d5b76024f0a9f1fa0de9d2dd7656d1303e0162132a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/bundle.json","state_url":"https://pith.science/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/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":"2026-07-06T18:32:06Z","links":{"resolver":"https://pith.science/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ","bundle":"https://pith.science/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/bundle.json","state":"https://pith.science/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZOXFXTMVOXOU4OYLS6MXL6VUDQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZOXFXTMVOXOU4OYLS6MXL6VUDQ","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":"b2c3def96ca7629ace530bdbc66fc962fc971c97f5ce8e935150f30b3db1782b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-07T12:49:14Z","title_canon_sha256":"6e55b53c601a8cf3a5f89270d3bf11bb7cae5cd81ac902b620b6a811d1bdb543"},"schema_version":"1.0","source":{"id":"2204.03410","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.03410","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"arxiv_version","alias_value":"2204.03410v7","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.03410","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZOXFXTMVOXOU","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_16","alias_value":"ZOXFXTMVOXOU4OYL","created_at":"2026-07-05T05:40:05Z"},{"alias_kind":"pith_short_8","alias_value":"ZOXFXTMV","created_at":"2026-07-05T05:40:05Z"}],"graph_snapshots":[{"event_id":"sha256:146c747a7fb4436cacf82d5b76024f0a9f1fa0de9d2dd7656d1303e0162132a3","target":"graph","created_at":"2026-07-05T05:40:05Z","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/2204.03410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Class incremental learning(CIL) has attracted much attention, but most existing related works focus on fine-tuning the entire representation model, which inevitably results in much catastrophic forgetting. In the contrast, with a semantic-rich pre-trained representation model, parameter-additional-tuning (PAT) only changes very few parameters to learn new visual concepts. Recent studies have proved that PAT-based CIL can naturally avoid fighting against forgetting by replaying or distilling like most of the existing methods. However, we find that PAT-based CIL still faces serious semantic drif","authors_text":"Haojian Zhang, Jianhua Hu, Jieren Deng, Yunkuan Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-07T12:49:14Z","title":"Incremental Prototype Tuning for Class Incremental Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.03410","kind":"arxiv","version":7},"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:ec67fa5e5511eb48cf6846501d322020de6d3f7f5d1fe8d7a0227b19b1b346ba","target":"record","created_at":"2026-07-05T05:40:05Z","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":"b2c3def96ca7629ace530bdbc66fc962fc971c97f5ce8e935150f30b3db1782b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-07T12:49:14Z","title_canon_sha256":"6e55b53c601a8cf3a5f89270d3bf11bb7cae5cd81ac902b620b6a811d1bdb543"},"schema_version":"1.0","source":{"id":"2204.03410","kind":"arxiv","version":7}},"canonical_sha256":"cbae5bcd9575dd4e3b0b979975fab41c2d091df58e3303894056def61d8e7b78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cbae5bcd9575dd4e3b0b979975fab41c2d091df58e3303894056def61d8e7b78","first_computed_at":"2026-07-05T05:40:05.270565Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:05.270565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ojCZZJbN3FUtkGs6yMWG8TTZBiv5pWOO80m/84z1BNsT5BMwIXnqo0h1PWilp2smDXfK98bTPB7Ok28FyvVKAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:05.271086Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.03410","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec67fa5e5511eb48cf6846501d322020de6d3f7f5d1fe8d7a0227b19b1b346ba","sha256:146c747a7fb4436cacf82d5b76024f0a9f1fa0de9d2dd7656d1303e0162132a3"],"state_sha256":"755514b9e03f3c684af25a7c7db792c203625222c813b06405fe3cf9b3c8158f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ghFo/GYkf6uGYgryK0/ey/C3lyDOJpEkBJAia5BQ4WoTxl/fuUNeQ+ZW4RLLVW/CpATZ4IscMcleUzgBodL9Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:32:06.667129Z","bundle_sha256":"47d7430881e70203ae3fe1c29dd7a4c0d93c3892603d77dfb7c6d93c1a428817"}}