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.

7 papers of 8,653Sort Recent · Most cited
  1. 2024
    Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot LearningVinay Kumar Verma, Nikhil Mehta, Kevin J Liang … Lawrence CarinWACV · Duke University · Kootenay Association for Science & Technology · +1
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  2. 2022PDF ↗
  3. 2021
    Efficient Feature Transformations for Discriminative and Generative Continual LearningVinay Kumar Verma, Kevin J Liang, Nikhil Mehta … Lawrence CarinCVPR · Duke University · Indian Institute of Technology Kanpur
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  4. 2021
    Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot LearningVinay Kumar Verma, Kevin J Liang, Nikhil Mehta, Lawrence CarinarXiv · Duke University
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  5. 2021
    Continual Learning using a Bayesian Nonparametric Dictionary of Weight FactorsNikhil Mehta, Kevin J Liang, V. Verma, L. CarinAISTATS
  6. 2020
    Bayesian Nonparametric Weight Factorization for Continual LearningNikhil Mehta, Kevin J Liang, Vinay Kumar Verma, Lawrence CarinarXiv
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  7. 2018
    Generative Adversarial Network Training is a Continual Learning ProblemKevin J Liang, Chunyuan Li, Guoyin Wang, Lawrence CarinarXiv
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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.