{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OP3EJKP6QFESESA5YMK64MDBIV","short_pith_number":"pith:OP3EJKP6","schema_version":"1.0","canonical_sha256":"73f644a9fe814922481dc315ee3061455e6f396d016e51d461f40e8a92b8bdce","source":{"kind":"arxiv","id":"2307.09218","version":3},"attestation_state":"computed","paper":{"title":"A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Enneng Yang, Heng Huang, Li Shen, Zhenyi Wang","submitted_at":"2023-07-16T16:27:58Z","abstract_excerpt":"Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new task, managing task interfer"},"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":"2307.09218","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-16T16:27:58Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"b613efa6a6c9c65bf0936d9c0f9e4bea0d62d15d51ba4a0089e2ecba5d5a85ee","abstract_canon_sha256":"cde7d5e604ecf4c93ed26d6a9d8055a56cf27bb336b06dabc0fa9c9834bd499f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:36.354074Z","signature_b64":"Y7ThTxHYlVIpqGzn0AvPiHzEaWAqyIsNha2jFaLozPCa7FVwIBRKZW3XuAtuEgTJTNwt/d1b7BQQ/BN4LBN0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73f644a9fe814922481dc315ee3061455e6f396d016e51d461f40e8a92b8bdce","last_reissued_at":"2026-07-05T09:36:36.353606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:36.353606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Enneng Yang, Heng Huang, Li Shen, Zhenyi Wang","submitted_at":"2023-07-16T16:27:58Z","abstract_excerpt":"Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new task, managing task interfer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09218","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/2307.09218/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":"2307.09218","created_at":"2026-07-05T09:36:36.353658+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.09218v3","created_at":"2026-07-05T09:36:36.353658+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09218","created_at":"2026-07-05T09:36:36.353658+00:00"},{"alias_kind":"pith_short_12","alias_value":"OP3EJKP6QFES","created_at":"2026-07-05T09:36:36.353658+00:00"},{"alias_kind":"pith_short_16","alias_value":"OP3EJKP6QFESESA5","created_at":"2026-07-05T09:36:36.353658+00:00"},{"alias_kind":"pith_short_8","alias_value":"OP3EJKP6","created_at":"2026-07-05T09:36:36.353658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.04638","citing_title":"No Forgetting Learning: Buffer-free Continual Learning Classification","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2408.07666","citing_title":"Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities","ref_index":246,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV","json":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV.json","graph_json":"https://pith.science/api/pith-number/OP3EJKP6QFESESA5YMK64MDBIV/graph.json","events_json":"https://pith.science/api/pith-number/OP3EJKP6QFESESA5YMK64MDBIV/events.json","paper":"https://pith.science/paper/OP3EJKP6"},"agent_actions":{"view_html":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV","download_json":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV.json","view_paper":"https://pith.science/paper/OP3EJKP6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.09218&json=true","fetch_graph":"https://pith.science/api/pith-number/OP3EJKP6QFESESA5YMK64MDBIV/graph.json","fetch_events":"https://pith.science/api/pith-number/OP3EJKP6QFESESA5YMK64MDBIV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV/action/storage_attestation","attest_author":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV/action/author_attestation","sign_citation":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV/action/citation_signature","submit_replication":"https://pith.science/pith/OP3EJKP6QFESESA5YMK64MDBIV/action/replication_record"}},"created_at":"2026-07-05T09:36:36.353658+00:00","updated_at":"2026-07-05T09:36:36.353658+00:00"}