Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

18 papers of 11,817Sort Recent · Most cited
  1. 2024
    Continual Learning With Knowledge Distillation: A SurveySong Li, Tonghua Su, Xu-Yao Zhang, Zhongjie WangTNNLS
  2. 2024
    Recent Advances of Multimodal Continual Learning: A Comprehensive SurveyDianzhi Yu, Xinni Zhang, Yankai Chen … Irwin KingTNNLS
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  4. 2024
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  6. 2024
    Enhancing Few-Shot CLIP With Semantic-Aware Fine-TuningYao Zhu, Yuefeng Chen, Xiaofeng Mao … Xiangyang JiTNNLS
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  7. 2024
    StaRS: Learning a Stable Representation Space for Continual Relation ClassificationNing Pang, Xiang Zhao, Weixin Zeng … Weidong XiaoTNNLS
  8. 2024
    Double Confidence Calibration Focused Distillation for Task-Incremental LearningZhiling Fu, Zhe Wang, Chengwei Yu … Dongdong LiTNNLS
  9. 2024
    Continual Learning With Unknown Task BoundaryXiaoxie Zhu, Jinfeng Yi, Lijun ZhangTNNLS
  10. 2024
    ScalableTrack: Scalable One-Stream Tracking via Alternating LearningHongmin Liu, Yuefeng Cai, Bin Fan, Jinglin XuTNNLS
  11. 2024
    Robust Incremental Broad Learning System for Data Streams of Uncertain ScaleLin-Jun Zhong, C. L. Philip Chen, Ji-Feng Guo, Tong ZhangTNNLS
  12. 2024
    DyCR: A Dynamic Clustering and Recovering Network for Few-Shot Class-Incremental LearningZicheng Pan, Xiaohan Yu, Miaohua Zhang … Yongsheng GaoTNNLS
  13. 2024
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  17. 2024
    Physics-Informed Explainable Continual Learning on GraphsCiyuan Peng, Tao Tang, Qiuyang Yin … Charu C. AggarwalTNNLS
  18. 2024
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.