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.

5 papers of 11,817Sort Recent · Most cited
  1. 2021
    Metric Learning with Distillation for Overcoming Catastrophic ForgettingPiaoyao Yu, Juanjuan He, Qilang Min, Qi ZhuSpringer CCIS · Wuhan University of Science and Technology
  2. 2021
    Regular Decision Processes for Grid WorldsNicky Lenaers, Martijn van OtterloSpringer CCIS · Open University of the Netherlands · Radboud University Nijmegen
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  3. 2021
    On robustness of generative representations against catastrophic forgettingWojciech Masarczyk, Kamil Rafał Deja, T. P. TrzcinskiSpringer CCIS · Warsaw University of Technology · Jagiellonian University
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  4. 2021
    Self-supervised Continual Learning for Object Recognition in Image SequencesRuiqi Dai, Mathieu Lefort, Frédéric Armetta … Stefan DuffnerSpringer CCIS · Lyon 1 Université · Centre National de la Recherche Scientifique · +3
  5. 2021
    Sparse Progressive Neural Networks for Continual LearningEsra Ergün, Behçet Uğur TöreyınSpringer CCIS · Istanbul Technical University
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.