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.

6 papers of 8,653Sort Recent · Most cited
  1. 2023
    What, How, and When Should Object Detectors Update in Continually Changing Test Domains?Jayeon Yoo, D.T. Lee, Inseop Chung … Nojun KwakCVPR · Seoul National University
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  2. 2024
    Class Incremental Learning With Large Domain ShiftKamin Lee, Hyoeun Kim, Geunjae Choi … Nojun KwakIEEE Access · Seoul National University of Science and Technology · LG (South Korea)
  3. 2022PDF ↗
  4. 2018
    StackNet: Stacking feature maps for Continual learningKim Jangho, Jeesoo Kim, Nojun KwakCVPR · Seoul National University
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  5. 2018
    StackNet: Stacking Parameters for Continual learningJangho Kim, Jeesoo Kim, Nojun KwakPreprint
  6. 2018
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.