{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SYLQGHDEFSLTS5XHG7MBRDUA55","short_pith_number":"pith:SYLQGHDE","schema_version":"1.0","canonical_sha256":"9617031c642c973976e737d8188e80ef783b0a71ea4152befd3dcd475f3895c9","source":{"kind":"arxiv","id":"1904.00310","version":3},"attestation_state":"computed","paper":{"title":"Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Caiming Xiong, Richard Socher, Tianfu Wu, Xilai Li, Yingbo Zhou","submitted_at":"2019-03-31T00:35:36Z","abstract_excerpt":"Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting problem. This paper presents a conceptually simple yet general and effective framework for handling catastrophic forgetting in continual learning with DNNs. The proposed method consists of two components: a neural structure optimization component and a parameter learning and/or fine-tu"},"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":"1904.00310","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-31T00:35:36Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"00ac3827e8981e4a262f67b97692c8536579228019f9ddb2e096909add61016a","abstract_canon_sha256":"f75abf1f2bde39a9676509edb7a110ba73fea3f591de5941eecbb134d96c0d7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:44.210741Z","signature_b64":"PpcTsq+SAqB04qN5/uxagIoU43hcABbqcC7V+aYxZNM7EhkzfnDaV3NoKeBUvlmwWLjtWYC+OkdotqK8v3zqCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9617031c642c973976e737d8188e80ef783b0a71ea4152befd3dcd475f3895c9","last_reissued_at":"2026-05-17T23:45:44.210068Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:44.210068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Caiming Xiong, Richard Socher, Tianfu Wu, Xilai Li, Yingbo Zhou","submitted_at":"2019-03-31T00:35:36Z","abstract_excerpt":"Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting problem. This paper presents a conceptually simple yet general and effective framework for handling catastrophic forgetting in continual learning with DNNs. The proposed method consists of two components: a neural structure optimization component and a parameter learning and/or fine-tu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00310","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":""},"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":"1904.00310","created_at":"2026-05-17T23:45:44.210169+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00310v3","created_at":"2026-05-17T23:45:44.210169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00310","created_at":"2026-05-17T23:45:44.210169+00:00"},{"alias_kind":"pith_short_12","alias_value":"SYLQGHDEFSLT","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SYLQGHDEFSLTS5XH","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SYLQGHDE","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18187","citing_title":"BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding","ref_index":111,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55","json":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55.json","graph_json":"https://pith.science/api/pith-number/SYLQGHDEFSLTS5XHG7MBRDUA55/graph.json","events_json":"https://pith.science/api/pith-number/SYLQGHDEFSLTS5XHG7MBRDUA55/events.json","paper":"https://pith.science/paper/SYLQGHDE"},"agent_actions":{"view_html":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55","download_json":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55.json","view_paper":"https://pith.science/paper/SYLQGHDE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00310&json=true","fetch_graph":"https://pith.science/api/pith-number/SYLQGHDEFSLTS5XHG7MBRDUA55/graph.json","fetch_events":"https://pith.science/api/pith-number/SYLQGHDEFSLTS5XHG7MBRDUA55/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55/action/storage_attestation","attest_author":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55/action/author_attestation","sign_citation":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55/action/citation_signature","submit_replication":"https://pith.science/pith/SYLQGHDEFSLTS5XHG7MBRDUA55/action/replication_record"}},"created_at":"2026-05-17T23:45:44.210169+00:00","updated_at":"2026-05-17T23:45:44.210169+00:00"}