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. 2026
    IncreFA: Breaking the Static Wall of Generative Model AttributionHaotian Qin, Dongliang Chang, Yueying Gao … Zhanyu MaCVPR
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  2. 2025
    A Selective Learning Method for Temporal Graph Continual LearningHanmo Liu, Di Su, Haoyang Li … Lei ChenICML
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  3. 2024
    Effective Data Selection and Replay for Unsupervised Continual LearningHanmo Liu, Shimin Di, Haoyang Li … Xiaofang ZhouICDE · Hong Kong University of Science and Technology · South China Normal University
  4. 2022
    Camel: Managing Data for Efficient Stream LearningYiming Li, Yanyan Shen, Lei Chen2022 International Conference on Management of Data · Hong Kong University of Science and Technology · Shanghai Jiao Tong University
  5. 2021
    Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned GenerationMengyao Zhai, Lei Chen, Greg MoriCVPR · Simon Fraser University
  6. 2020
    Piggyback GAN: Efficient Lifelong Learning for Image Conditioned GenerationMengyao Zhai, Lei Chen, Jiawei He … Greg MoriECCV · Simon Fraser University · Collège Boréal
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  7. 2019
    Lifelong GAN: Continual Learning for Conditional Image GenerationMengyao Zhai, Lei Chen, Fred Tung … Greg MoriICCV · Simon Fraser University · Borealis (Austria)
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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.