{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NBJPKMXAXRFFUAGIXVPKOGAWUD","short_pith_number":"pith:NBJPKMXA","schema_version":"1.0","canonical_sha256":"6852f532e0bc4a5a00c8bd5ea71816a0e4892677c2ab3c11eeea09ee911f2308","source":{"kind":"arxiv","id":"1810.12488","version":4},"attestation_state":"computed","paper":{"title":"Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Anita Ramasamy, Yen-Chang Hsu, Yen-Cheng Liu, Zsolt Kira","submitted_at":"2018-10-30T02:08:35Z","abstract_excerpt":"Continual learning has received a great deal of attention recently with several approaches being proposed. However, evaluations involve a diverse set of scenarios making meaningful comparison difficult. This work provides a systematic categorization of the scenarios and evaluates them within a consistent framework including strong baselines and state-of-the-art methods. The results provide an understanding of the relative difficulty of the scenarios and that simple baselines (Adagrad, L2 regularization, and naive rehearsal strategies) can surprisingly achieve similar performance to current mai"},"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":"1810.12488","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-30T02:08:35Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"586dea45ff66fd5e1462a080e354fb38f0c473e7ee29dd2d461f9a9332bbf8ec","abstract_canon_sha256":"0087cfadc2e51e5d37e61256654b9cdb593c8d0e4b1f8c1ab21ab1b51ea32115"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:42.492075Z","signature_b64":"IWeukM8vBXC9LjTgiEOa+WoZc45C14N92wdlVdM/P5UPRzAJlPRVwhTan6+W5uEJFglwDHSv2KUltrSPCVr2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6852f532e0bc4a5a00c8bd5ea71816a0e4892677c2ab3c11eeea09ee911f2308","last_reissued_at":"2026-05-17T23:55:42.491655Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:42.491655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Anita Ramasamy, Yen-Chang Hsu, Yen-Cheng Liu, Zsolt Kira","submitted_at":"2018-10-30T02:08:35Z","abstract_excerpt":"Continual learning has received a great deal of attention recently with several approaches being proposed. However, evaluations involve a diverse set of scenarios making meaningful comparison difficult. This work provides a systematic categorization of the scenarios and evaluates them within a consistent framework including strong baselines and state-of-the-art methods. The results provide an understanding of the relative difficulty of the scenarios and that simple baselines (Adagrad, L2 regularization, and naive rehearsal strategies) can surprisingly achieve similar performance to current mai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12488","kind":"arxiv","version":4},"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":"1810.12488","created_at":"2026-05-17T23:55:42.491724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.12488v4","created_at":"2026-05-17T23:55:42.491724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12488","created_at":"2026-05-17T23:55:42.491724+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBJPKMXAXRFF","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBJPKMXAXRFFUAGI","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBJPKMXA","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"1907.07872","citing_title":"Autoencoder-Based Incremental Class Learning without Retraining on Old Data","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2505.18604","citing_title":"Exemplar-Free Continual Learning for State Space Models","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2506.21872","citing_title":"A Survey of Continual Reinforcement Learning","ref_index":50,"is_internal_anchor":true},{"citing_arxiv_id":"2601.13844","citing_title":"Optimal L2 Regularization in High-dimensional Continual Linear Regression","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2602.08813","citing_title":"Robust Policy Optimization to Prevent Catastrophic Forgetting","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11804","citing_title":"Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04059","citing_title":"Continual Distillation of Teachers from Different Domains","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07266","citing_title":"Tracking Adaptation Time: Metrics for Temporal Distribution Shift","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22838","citing_title":"Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21927","citing_title":"Fine-Tuning Regimes Define Distinct Continual Learning Problems","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21930","citing_title":"Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD","json":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD.json","graph_json":"https://pith.science/api/pith-number/NBJPKMXAXRFFUAGIXVPKOGAWUD/graph.json","events_json":"https://pith.science/api/pith-number/NBJPKMXAXRFFUAGIXVPKOGAWUD/events.json","paper":"https://pith.science/paper/NBJPKMXA"},"agent_actions":{"view_html":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD","download_json":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD.json","view_paper":"https://pith.science/paper/NBJPKMXA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.12488&json=true","fetch_graph":"https://pith.science/api/pith-number/NBJPKMXAXRFFUAGIXVPKOGAWUD/graph.json","fetch_events":"https://pith.science/api/pith-number/NBJPKMXAXRFFUAGIXVPKOGAWUD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD/action/storage_attestation","attest_author":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD/action/author_attestation","sign_citation":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD/action/citation_signature","submit_replication":"https://pith.science/pith/NBJPKMXAXRFFUAGIXVPKOGAWUD/action/replication_record"}},"created_at":"2026-05-17T23:55:42.491724+00:00","updated_at":"2026-05-17T23:55:42.491724+00:00"}