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. 2019
    Class-incremental Learning via Deep Model ConsolidationJunting Zhang, Jie Zhang, Shalini Ghosh … C.‐C. Jay KuoWACV · University of Southern California · California Southern University · +3
    PDF ↗
  2. 2020
    Regularize, Expand and Compress: NonExpansive Continual LearningJie Zhang, Junting Zhang, Shalini Ghosh … Yalin WangWACV · University of Southern California · Arizona State University · +1
  3. 2019
    RILOD: near real-time incremental learning for object detection at the edgeDawei Li, Şerafettin Taşcı, Shalini Ghosh … Larry HeckACM/IEEE Symposium on Edge Computing · Samsung (United States) · Research!America (United States) · +2
  4. 2019
    Efficient Incremental Learning for Mobile Object DetectionDawei Li, Şerafettin Taşcı, Shalini Ghosh … Larry HeckarXiv · Samsung (United States) · Research!America (United States) · +3
    PDF ↗
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.