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.

7 papers of 11,817Sort Recent · Most cited
  1. 2022
    TAME: Task Agnostic Continual Learning using Multiple ExpertsHaoran Zhu, Maryam Majzoubi, Arihant Jain, Anna ChoromanskaCVPR · New York University · Google (United States)
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  2. 2020
    Overcoming Catastrophic Forgetting via Direction-Constrained OptimizationYunfei Teng, Anna Choromanska, Murray Campbell … Lior HoreshSpringer LNCS · New York University
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  3. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  4. 2021
    Few-Shot Continual Learning for Audio ClassificationYu Wang, Nicholas J. Bryan, Mark Cartwright … Justin SalamonICASSP · New York University · Adobe Systems (United States)
  5. 2020
    AdapterFusion: Non-Destructive Task Composition for Transfer LearningJonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé … Iryna GurevychEACL · Technische Universität Darmstadt · Supélec · +5
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  6. 2019
    The role of prior knowledge in incremental associative learning: An empirical and computational approachOded Bein, Maayan Trzewik, Anat MarilJournal of Memory and Language · New York University · Tel Aviv University · +1
  7. 2019
    Task representations in neural networks trained to perform many cognitive tasksGuangyu Robert Yang, Madhura R. Joglekar, Hui Song … Xiao‐Jing WangNature Neuroscience · New York University · Columbia University · +5
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.