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. 2025
    Controllable Forgetting Mechanism for Few-Shot Class-Incremental LearningKirill Paramonov, Mete Özay, Eunju Yang … Umberto MichieliICASSP · Samsung (United Kingdom) · Samsung (South Korea)
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  2. 2024
    Learning With Style: Continual Semantic Segmentation Across Tasks and DomainsMarco Toldo, Umberto Michieli, Pietro ZanuttighTPAMI · University of Padua
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  3. 2024PDF ↗
  4. 2023
    Online Continual Learning for Robust Indoor Object RecognitionUmberto Michieli, Mete ÖzayIROS · Samsung (United Kingdom)
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  5. 2022
    Domain adaptation and continual learning in semantic segmentationUmberto Michieli, Marco Toldo, Pietro ZanuttighElsevier · University of Padua
  6. 2021
    RECALL: Replay-based Continual Learning in Semantic SegmentationAndrea Maracani, Umberto Michieli, Marco Toldo, Pietro ZanuttighICCV · University of Padua
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  7. 2021PDF ↗
  8. 2021
    Knowledge Distillation for Incremental Learning in Semantic SegmentationUmberto Michieli, Pietro ZanuttighComputer Vision and Image Understanding · University of Padua
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  9. 2019
    Incremental Learning Techniques for Semantic SegmentationUmberto Michieli, Pietro ZanuttighICCV · University of Padua
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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.