Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

3 papers of 8,653Sort Recent · Most cited
  1. 2025
    Rethinking Continual Learning with Pre-Trained Models: Knowledge-Preserving ApproachMakoto Misaizu, Koshi Watanabe, Keisuke Maeda … Miki HaseyamaIEEE 14th Global Conference on Consumer Electronics (GCCE) · Hokkaido University
  2. 2025
    Analysis of Model Merging for Open-Vocabulary Models with Parameter Efficient Fine-Tuning Leveraging Distributed DataKenta Kubota, Ren Togo, Keisuke Maeda … Miki HaseyamaIEEE International Conference on Consumer Electronics - T… · Hokkaido University of Science · Hokkaido University
  3. 2025
    Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data SettingsKenta Kubota, Ren Togo, Keisuke Maeda … Miki HaseyamaApplied Sciences · Hokkaido University
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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.