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.

5 papers of 11,817Sort Recent · Most cited
  1. 2022
    Increasing Depth of Neural Networks for Life-long LearningJędrzej Kozal, Michał WoźniakInformation Fusion · Wrocław University of Science and Technology · AGH University of Krakow
    PDF ↗
  2. 2022
    Tracking changes using Kullback-Leibler divergence for the continual learningSebastián Basterrech, Michał WoźniakIEEE International Conference on Systems, Man, and Cybern… · VSB - Technical University of Ostrava · Wrocław University of Science and Technology · +1
    PDF ↗
  3. 2021
    FILDNE: A Framework for Incremental Learning of Dynamic Networks EmbeddingsPiotr Bielak, Kamil Tagowski, Maciej Falkiewicz … Nitesh V. ChawlaKnowledge-Based Systems · Wrocław University of Science and Technology · AGH University of Krakow · +1
    PDF ↗
  4. 2015
    Reacting to different types of concept drift with adaptive and incremental one-class classifiersBartosz Krawczyk, Michał WoźniakIEEE 2nd International Conference on Cybernetics (CYBCONF) · Wrocław University of Science and Technology
  5. 2015
    One-class classifiers with incremental learning and forgetting for data streams with concept driftBartosz Krawczyk, Michał WoźniakSoft Computing · Wrocław University of Science and Technology
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.