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. 2026
    Continual learning for autoregressive PDE surrogates under evolving physical regimesHamed Hemati, Binh Duong Nguyen, Stefan SandfeldMachine learning for computational science and engineering · Forschungszentrum Jülich · RWTH Aachen University
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  2. 2025
    Continual Learning in the Presence of RepetitionHamed Hemati, Lorenzo Pellegrini, X.W. Duan … Gido M. van de VenNeural Networks · University of St.Gallen · University of Applied Sciences St. Gallen · +10
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  3. 2024PDF ↗
  4. 2023
    A Comprehensive Empirical Evaluation on Online Continual LearningAlbin Soutif--Cormerais, Antonio Carta, Andrea Cossu … Hamed HematiICCV · Computer Vision Center · University of Pisa · +2
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  5. 2023
    Partial Hypernetworks for Continual LearningHamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian BorthCoLLAs
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  6. 2023
    Avalanche: A PyTorch Library for Deep Continual LearningAntonio Carta, Lorenzo Pellegrini, Andrea Cossu … Vincenzo LomonacoJMLR
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  7. 2023
    Class-Incremental Learning with RepetitionHamed Hemati, Andrea Cossu, Antonio Carta … Damian BorthCoLLAs
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  8. 2021
    Continual Speaker Adaptation for Text-to-Speech SynthesisHamed Hemati, Damian BortharXiv · University of St.Gallen
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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. 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.