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.

11 papers of 11,817Sort Recent · Most cited
  1. 2025PDF ↗
  2. 2025PDF ↗
  3. 2025
    Topological Mapping and Continual LearningNaoyuki Kubota, Takenori Obo, Yuichiro Toda, Naoki MasuyamaJournal of Japan Society for Fuzzy Theory and Intelligent…
  4. 2025
  5. 2024
  6. 2024
  7. 2022
    Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological ClusteringNaoki Masuyama, Yusuke Nojima, Farhan Dawood, Zongying LiuApplied Sciences · Osaka Metropolitan University · Tokyo Metropolitan University · +3
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  8. 2023
    Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryNaoki Masuyama, Y. Nojima, Yuichiro Toda … N. KubotaIEEE Access
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  9. 2023
    A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual LearningNaoki Masuyama, Takanori Takebayashi, Y. Nojima … Stefan WermterNeural computing & applications (Print)
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  10. 2021
    Multi-Label Classification via Adaptive Resonance Theory-Based ClusteringNaoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao IshibuchiTPAMI · Osaka Metropolitan University · University of Malaya · +1
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  11. 2022
    Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual LearningNaoki Masuyama, Narito Amako, Yuna Yamada … Hisao IshibuchiIEEE Access · Osaka Metropolitan University · Osaka Prefecture University · +1
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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.