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.

7 papers of 8,653Sort Recent · Most cited
  1. 2025
    Topological Mapping and Continual LearningNaoyuki Kubota, Takenori Obo, Yuichiro Toda, Naoki MasuyamaJournal of Japan Society for Fuzzy Theory and Intelligent… · Tokyo Metropolitan University · Okayama University · +1
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  2. 2025
    Neuro-Mimetic Developmental Architecture for Continual Learning Through Self-Organizing Multimodal Perception CoordinationFarhan Dawood, Naoki Masuyama, Chu Kiong LooIEEE Access · Iqra University · Osaka Metropolitan University · +1
  3. 2024
    Clustering-Based Automatic Codeword Lengths Determination in Self-Supervised LearningTakanori Takebayashi, Naoki Masuyama, Yusuke NojimaICML · Osaka Metropolitan University · Tokyo Metropolitan University
  4. 2024
    Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryNaoki Masuyama, Yusuke Nojima, Yuichiro Toda … Naoyuki KubotaIEEE Access · Osaka Metropolitan University · Okayama University · +3
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  5. 2023
    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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  6. 2023
    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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  7. 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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.