Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 5,456Sort Recent · Most cited
  1. 2023
    Dealing with Cross-Task Class Discrimination in Online Continual LearningYiduo Guo, Bing Liu, Dongyan ZhaoCVPR · Peking University · University of Illinois Chicago
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  2. 2023
    Rebalancing Batch Normalization for Exemplar-Based Class-Incremental LearningSungmin Cha, Sungjun Cho, Dasol Hwang … Taesup MoonCVPR · Seoul National University · University of Illinois Chicago
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  3. 2023
    AI Autonomy: Self-initiated Open-world Continual Learning and AdaptationBing Liu, Sahisnu Mazumder, Eric Robertson, Scott S. GrigsbyAI Magazine · University of Illinois Chicago · Intelligent Systems Research (United States) · +2
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  4. 2023
    Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCheng Ji, Jianxin Li, Hao Peng … Philip S. YuWSDM · Beihang University · Macquarie University · +1
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  5. 2023
    Improving Gender Fairness of Pre-Trained Language Models without Catastrophic ForgettingZahra Fatemi, Xing Chen, Wenhao Liu, Caimming XiongACL · University of Illinois Chicago · University of Chicago · +1
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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 lists only 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. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.