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.

7 papers of 11,817Sort Recent · Most cited
  1. 2021
    Continually Learning Self-Supervised Representations with Projected Functional RegularizationAlex Gomez-Villa, Bartłomiej Twardowski, Yu Lu … Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Tianjin University of Technology · +1
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  2. 2021
    ACAE-REMIND for Online Continual Learning with Compressed Feature ReplayKai Wang, Joost van de Weijer, Luis HerranzPattern Recognition Letters · Universitat Autònoma de Barcelona · Computer Vision Center
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  3. 2021
    On the importance of cross-task features for class-incremental learningAlbin Soutif--Cormerais, Marc Masana, Joost van de Weijer, Bartłomiej TwardowskiarXiv
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  4. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  5. 2021
    Ternary Feature Masks: zero-forgetting for task-incremental learningMarc Masana, Tinne Tuytelaars, Joost van de WeijerCVPR · Universitat Autònoma de Barcelona · Barcelona Supercomputing Center · +2
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  6. 2021
    Continual learning in cross-modal retrievalKai Wang, Luis Herranz, Joost van de WeijerCVPR · Universitat Autònoma de Barcelona
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  7. 2021
    HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined ClassificationKai Wang, Xialei Liu, Luis Herranz, Joost van de WeijerBMVC · Universitat Autònoma de Barcelona · Nankai University
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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.