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.

8 papers of 8,653Sort Recent · Most cited
  1. 2023
    CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in RoboticsAli Ayub, Alan R. WagnerIEEE TCDS · University of Waterloo · Concordia University · +1
    PDF ↗
  2. 2022
    Few-Shot Continual Active Learning by a RobotAli Ayub, Carter FendleyNeurIPS
    PDF ↗
  3. 2021
    F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object LearningAli Ayub, Alan R. WagnerICRA · Pennsylvania State University
    PDF ↗
  4. 2021
    Learning Novel Objects Continually Through CuriosityAli Ayub, Alan R. WagnerarXiv · Pennsylvania State University
    PDF ↗
  5. 2021
    Continual Learning of Visual Concepts for Robots through Limited SupervisionAli Ayub, Alan R. WagnerIEEE/ACM International Conference on Human-Robot Interaction · Pennsylvania State University
    PDF ↗
  6. 2021
    EEC: Learning to Encode and Regenerate Images for Continual LearningAli Ayub, Alan R. WagnerICLR · Pennsylvania State University
    PDF ↗
  7. 2020
    Storing Encoded Episodes as Concepts for Continual LearningAli Ayub, Alan R. WagnerarXiv · Pennsylvania State University
    PDF ↗
  8. 2020
    Cognitively-Inspired Model for Incremental Learning Using a Few ExamplesAli Ayub, Alan R. WagnerCVPR · Pennsylvania State University
    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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.