{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-fast-learning-slow-a-general-1","title":"Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System","arxiv_id":"2201.12604","date":"2022-01-29","proceeding":"ICLR 2022 4","authors":["Elahe Arani","Fahad Sarfraz","Bahram Zonooz"],"abstract":"Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting. The complementary learning system (CLS) theory suggests that the interplay between rapid instance-based learning and slow structured learning in the brain is crucial for accumulating and retaining knowledge. Here, we propose CLS-ER, a novel dual memory experience replay (ER) method which maintains short-term and long-term semantic memories that interact with the episodic memory. Our method employs an effective replay mechanism whereby new knowledge is acquired while aligning the decision boundaries with the semantic memories. CLS-ER does not utilize the task boundaries or make any assumption about the distribution of the data which makes it versatile and suited for \"general continual learning\". Our approach achieves state-of-the-art performance on standard benchmarks as well as more realistic general continual learning settings.","url_abs":"https://arxiv.org/abs/2201.12604v2","url_pdf":"https://arxiv.org/pdf/2201.12604v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-fast-learning-slow-a-general-1","repo_url":"https://github.com/NeurAI-Lab/CLS-ER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"}],"methods":[{"method_slug":"experience-replay","method_name":"Experience Replay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.12604","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12604"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/NeurAI-Lab/CLS-ER","reach":null}],"summary":{"ran":3,"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"cbf06486bbe7b6ac","entry":"Buffer","repo":"NeurAI-Lab/CLS-ER","repo_kind":"official","path":"models/clser.py","file_url":"https://github.com/NeurAI-Lab/CLS-ER/blob/HEAD/models/clser.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cbf06486bbe7b6ac"}},{"code_sha256_prefix":"364c8278b0b06479","entry":"ContinualModel","repo":"NeurAI-Lab/CLS-ER","repo_kind":"official","path":"models/clser.py","file_url":"https://github.com/NeurAI-Lab/CLS-ER/blob/HEAD/models/clser.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"364c8278b0b06479"}},{"code_sha256_prefix":"a3d41d3bc1c22b19","entry":"get_device","repo":"NeurAI-Lab/CLS-ER","repo_kind":"official","path":"models/clser.py","file_url":"https://github.com/NeurAI-Lab/CLS-ER/blob/HEAD/models/clser.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a3d41d3bc1c22b19"}},{"code_sha256_prefix":"7e522e4817696b98","entry":"reservoir","repo":"NeurAI-Lab/CLS-ER","repo_kind":"official","path":"models/clser.py","file_url":"https://github.com/NeurAI-Lab/CLS-ER/blob/HEAD/models/clser.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7e522e4817696b98"}},{"code_sha256_prefix":"dc676575ec86ed7a","entry":"CLSER","repo":"NeurAI-Lab/CLS-ER","repo_kind":"official","path":"models/clser.py","file_url":"https://github.com/NeurAI-Lab/CLS-ER/blob/HEAD/models/clser.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dc676575ec86ed7a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}