Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,493Sort Recent · Most cited
  1. 2026
    Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel PerspectiveJingren Liu, Zhong Ji, Yunlong Yu … Xuelong LiTPAMI · Tianjin University · Zhejiang University · +3
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  2. 2026
    Multi-Stage Knowledge Integration of Vision-Language Models for Continual LearningHongsheng Zhang, Zhong Ji, Jingren Liu … Jungong HanTIP · Tianjin University · Tsinghua University
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  3. 2024
    On the Approximation Risk of Few-Shot Class-Incremental LearningXuan Wang, Zhong Ji, Xiyao Liu … Jungong HanECCV · Tianjin University · Beijing Academy of Artificial Intelligence · +4
  4. 2025
    Imbalance Mitigation for Continual Learning via Knowledge Decoupling and Dual Enhanced Contrastive LearningZhong Ji, Zhanyu Jiao, Qiang Wang … Jungong HanTNNLS · Tianjin University · Beijing Academy of Artificial Intelligence · +2
  5. 2024
    Model Attention Expansion for Few-Shot Class-Incremental LearningXuan Wang, Zhong Ji, Yunlong Yu … Jungong HanTIP · Tianjin University · Zhejiang University · +1
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 led 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.