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.

4 papers of 8,653Sort Recent · Most cited
  1. 2025
    Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual LearningHuiyi Wang, Haodong Lu, Lina Yao, Dong GongCVPR · UNSW Sydney · Commonwealth Scientific and Industrial Research Organisation · +1
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  2. 2022
    Learning Bayesian Sparse Networks with Full Experience Replay for Continual LearningQingsen Yan, Dong Gong, Yuhang Liu … Qinfeng ShiCVPR · Australian Centre for Robotic Vision · The University of Adelaide · +1
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  3. 2022
    Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight RegularizationFrancesco Pelosin, Saurav Jha, Andrea Torsello … Joost van de WeijerCVPR · Ca' Foscari University of Venice · UNSW Sydney · +2
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  4. 2021
    Plastic and Stable Gated Classifiers for Continual LearningNicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier … Hanna SuominenCVPR · Australian National University · UNSW Sydney · +5
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.