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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Addressing catastrophic forgetting in class-incremental learning—a surveyJohn Bako, Jugal KalitaArtificial Intelligence Review · University of Colorado Colorado Springs
  2. 2025
    Generative Network Correction to Promote Incremental LearningJustin Leo, Jugal KalitaIEEE TETCI · University of Colorado Colorado Springs
  3. 2022
    Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic ForgettingGabriel Mantione-Holmes, Justin Leo, Jugal KalitaIEEE 17th International Conference on Semantic Computing… · Lewis & Clark College · University of Colorado Boulder · +2
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  4. 2021
    Incremental Deep Neural Network Learning Using Classification Confidence ThresholdingJustin Leo, Jugal KalitaTNNLS · University of Colorado Colorado Springs
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  5. 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.