Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 11,817Sort Recent · Most cited
  1. 2022
    Challenging Common Assumptions about Catastrophic Forgetting and Knowledge AccumulationTimothée Lesort, Оleksiy Ostapenko, Diganta Misra … Irina RishCoLLAs
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  2. 2022
    Continual Learning with Foundation Models: An Empirical Study of Latent ReplayОleksiy Ostapenko, Timothée Lesort, Pau Rodríguez … Laurent CharlinCoLLAs
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  3. 2022
    Foundational Models for Continual Learning: An Empirical Study of Latent ReplayOleksiy Ostapenko, Timothée Lesort, P. Rodríguez … Laurent CharlinarXiv
  4. 2022
    Scaling the Number of Tasks in Continual LearningTimothée Lesort, Oleksiy Ostapenko, Diganta Misra … I. RisharXiv
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.