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.

6 papers of 11,817Sort Recent · Most cited
  1. 2022
    Towards Building a Distributed Virtual Flow Meter via Compressed Continual LearningHasan Asyari Arief, Peter J. Thomas, Kevin Constable, Aggelos K. KatsaggelosSensors · Northwestern University · NORCE Research AS · +1
  2. 2020
    Efficient Architecture Search for Continual LearningQiang Gao, Zhipeng Luo, Diego Klabjan, Fengli ZhangTNNLS · Southwestern University of Finance and Economics · Northwestern University · +1
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  3. 2021
    Continual Neural Network Model RetrainingXiaofeng Zhu, Diego KlabjanIEEE International Conference on Big Data (Big Data) · Microsoft (United States) · Northwestern University
  4. 2021
    FLAR: A Unified Prototype Framework for Few-sample Lifelong Active RecognitionLei Fan, Peixi Xiong, Wei Wei, Ying WuICCV · Northwestern University
  5. 2019
    Frosting Weights for Better Continual TrainingXiaofeng Zhu, Feng Liu, Goce Trajcevski, Dingding WangICML · Northwestern University · Florida Atlantic University · +1
    PDF ↗
  6. 2007
    Incremental Learning of Perceptual Categories for Open-Domain Sketch RecognitionAndrew Lovett, Morteza Dehghani, Kenneth D. ForbusUS Dept of the Navy · Northwestern 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.