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.

3 papers of 11,817Sort Recent · Most cited
  1. 2021
    Deep Rapid Class Augmentation; A New Progressive Learning Approach that Eliminates the Issue of Catastrophic ForgettingHanna WitzgallAdvances in intelligent systems and computing · Leidos (United States)
  2. 2020
    Deep Neural Networks: Incremental LearningRama Murthy Garimella, Maha Lakshmi Bairaju, G. C. Jyothi Prasanna … Manasa JagannadanAdvances in intelligent systems and computing · Mahindra University · Rajiv Gandhi University of Knowledge Technologies
  3. 2019
    Moving Towards Open Set Incremental Learning: Readily Discovering New AuthorsJustin Leo, Jugal KalitaAdvances in intelligent systems and computing · University of Colorado Colorado Springs
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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.