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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Beyond catastrophic forgetting: A continual learning-driven multi-modal fusion model for saliency prediction in dynamic scenesNana Zhang, Yi-Xiang Wang, Dandan Zhu … Guangtao ZhaiExpert Systems with Applications · Donghua University · Tongji University · +1
  2. 2026
    Developing Evolving Adaptability in Biological Intelligence: A Novel Biologically-Inspired Continual Learning Model for Video Saliency PredictionDandan Zhu, Kaiwei Zhang, Kun Zhu … Xiaokang YangTPAMI · East China Normal University · Shanghai Artificial Intelligence Laboratory · +3
  3. 2024
    Few-Shot Class-Incremental Learning with Prior KnowledgeWenhao Jiang, Duo Li, Menghan Hu … Xiaoping ZhangarXiv
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  4. 2021
    Task-Specific Normalization for Continual Learning of Blind Image Quality ModelsWeixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang YangTIP · Shanghai Jiao Tong University · City University of Hong Kong · +1
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  5. 2023
    Continual Learning for Blind Image Quality AssessmentWeixia Zhang, Dingquan Li, Chao Ma … Kede MaTPAMI · Shanghai Jiao Tong University · Peng Cheng Laboratory · +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. 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.