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
    Negative-weighted knowledge distillation regularized graph convolutional network for multi-label class-incremental learningKaile Du, Junzhou Xie, Fan Lyu … Guangcan LiuPattern Recognition · Southeast University · Institute of Automation · +1
  2. 2023
    Low-redundancy distillation for continual learningRuiqi Liu, Boyu Diao, Libo Huang … Yongjun XuPattern Recognition · Chinese Academy of Sciences · Institute of Computing Technology · +1
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  3. 2024
    Domain-incremental learning without forgetting based on random vector functional link networksChengRan Liu, Yi Wang, Dong Li, Xizhao WangPattern Recognition · Chinese Academy of Sciences · Institute of Computing Technology · +2
  4. 2024
    Large-scale continual learning for ancient Chinese character recognitionYue Xu, Xu-Yao Zhang, Zhaoxiang Zhang, Cheng‐Lin LiuPattern Recognition · Chinese Academy of Sciences · Institute of Automation · +1
  5. 2021
    Multi-View Correlation Distillation for Incremental Object DetectionDongbao Yang, Yu Zhou, Aoting Zhang … Qixiang YePattern Recognition · Chinese Academy of Sciences · Institute of Information Engineering · +1
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  6. 2022
    Lifelong robotic visual-tactile perception learningJiahua Dong, Yang Cong, Gan Sun, Tao ZhangPattern Recognition · Shenyang Institute of Automation · Chinese Academy of Sciences · +1
  7. 2018
    A novel random forests based class incremental learning method for activity recognitionChunyu Hu, Yiqiang Chen, Lisha Hu, Xiaohui PengPattern Recognition · Chinese Academy of Sciences · Institute of Computing Technology · +2
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.