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.

4 papers of 8,653Sort Recent · Most cited
  1. 2025
    Navigating Semantic Drift in Task-Agnostic Class-Incremental LearningFangwen Wu, Lechao Cheng, Shengeng Tang … Meng WangICML
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  2. 2021
    Continual Neural Network Model RetrainingXiaofeng Zhu, Diego KlabjanIEEE International Conference on Big Data (Big Data) · Microsoft (United States) · Northwestern University
  3. 2020
    Neural Network Retraining for Model ServingDiego Klabjan, Xiaofeng ZhuarXiv
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  4. 2019
    Frosting Weights for Better Continual TrainingXiaofeng Zhu, Feng Liu, Goce Trajcevski, Dingding WangICML · Northwestern University · Florida Atlantic 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.