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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Unsupervised Continual Learning: A Review of Challenges, Techniques, and Evaluation MetricsHang Ruan, Tomás Maul, Yifan Chen … Man ZhouIEEE Access · University of Nottingham Malaysia Campus · Huazhong University of Science and Technology
  2. 2021
    Dynamic sampling of images from various categories for classification based incremental deep learning in fog computingSwaraj Dube, Yee Wan Wong, Hermawan NugrohoPeerJ Computer Science · University of Nottingham Malaysia Campus
  3. 2021
    A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud SystemSwaraj Dube, Wong Yee Wan, Hermawan NugrohoIEEE Access · University of Nottingham Malaysia Campus
  4. 2019
    Reducing Catastrophic Forgetting in Modular Neural Networks by Dynamic Information BalancingMohammed Amer, Tomás MaularXiv · University of Nottingham Malaysia Campus
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  5. 2011
    Radial Basis Function Neural Network With Incremental Learning for Face RecognitionYee Wan Wong, Kah Phooi Seng, Li-Minn AngIEEE Transactions · University of Nottingham Malaysia Campus
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.