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.

9 papers of 11,817Sort Recent · Most cited
  1. 2026
  2. 2026
    GE-PEFT: Gated Expandable Parameter-Efficient Fine-Tuning for Continual LearningJanna Omeliyanenko, Andreas Hotho, Daniel SchlörMachine-mediated learning
  3. 2026
    Class Incremental Learning and Auxiliary Unlabelled Data: The Importance of Neutral ExamplesIgor Sieradzki, Łukasz Struski, Igor T. Podolak, R. JanikMachine-mediated learning
  4. 2025
    SAMix: Calibrated and Accurate Continual Learning via Sphere-Adaptive Mixup and Neural CollapseTrung-Anh Dang, Vincent Nguyen, Ngoc-Son Vu, Christel VrainMachine-mediated learning
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  5. 2025
    Compression and restoration: exploring elasticity in continual test-time adaptationJingwei Li, Chengbao Liu, Xiwei Bai … Yudong WangMachine-mediated learning
  6. 2024
    Adaptive adapter routing for long-tailed class-incremental learningZhi-Hong Qi, Da-Wei Zhou, Yiran Yao … De-Chuan ZhanMachine-mediated learning
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  7. 2023
    Continual variational dropout: a view of auxiliary local variables in continual learningNam Le Hai, T. Nguyen, L. Van … K. ThânMachine-mediated learning
  8. 2023
    FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learningLing-Zhi Gao, Zexi Li, Xinyi Shang … Chao WuMachine-mediated learning
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  9. 2023
    From MNIST to ImageNet and back: benchmarking continual curriculum learningKamil Faber, Dominik Żurek, Marcin Pietroń … Roberto CorizzoMachine-mediated learning
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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.