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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026
    Policy Gradient with Adaptive Entropy Annealing for Continual Fine-TuningYaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert BifetarXiv
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  2. 2025
    CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in PythonHeitor Murilo Gomes, Anton Lee, Nuwan Gunasekara … Albert BifetarXiv · Centre National de la Recherche Scientifique · Sorbonne Université
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  3. 2025
    Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the PastYaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert BifetTrans. Mach. Learn. Res.
  4. 2023
    Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental LearningAnton Lee, Yaqian Zhang, Heitor Murilo Gomes … Bernhard PfahringerCIKM · Victoria University of Wellington · University of Waikato
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  5. 2023
    Survey on Online Streaming Continual LearningNuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert BifetIJCAI · University of Waikato · Victoria University of Wellington · +2
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  6. 2022
    Repeated Augmented Rehearsal: A Simple but Strong Baseline for Online Continual LearningYaqian Zhang, Bernhard Pfahringer, Eibe Frank … Yunzhe JiaNeurIPS
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  7. 2022
    Adaptive Online Domain Incremental Continual LearningNuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard PfahringerSpringer LNCS · University of Waikato
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  8. 2022
    Adaptive Neural Networks for Online Domain Incremental Continual LearningNuwan Gunasekara, Heitor Murilo Gomes, Albert Bifet, Bernhard PfahringerSpringer LNCS · University of Waikato
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  9. 2022
    A simple but strong baseline for online continual learning: Repeated Augmented RehearsalYaqian Zhang, Bernhard Pfahringer, Eibe Frank … Yunzhe JiaNeurIPS
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.