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.

10 papers of 6,984Sort Recent · Most cited
  1. 2025
    Generating samples for covariance to update prototype in few-shot class-incremental learningHong Yu, Qiwei Luo, Ye Wang, Guoyin WangApplied Intelligence · Chongqing University of Posts and Telecommunications · Chongqing Normal University
  2. 2025
    Exploring multi-granularity balance strategy for class incremental learning via three-way granular computingYan Xian, Hong Yu, Ye Wang, Guoyin WangBrain Informatics · Chongqing University of Posts and Telecommunications · Chongqing Normal University
  3. 2025
    A Novel Class Incremental Learning Method via Multi-granularity Balance Inspired by Human Granular Cognition MechanismYan Xian, Hong Yu, Ye Wang, Guoyin WangSpringer LNCS · Chongqing University of Posts and Telecommunications · Chongqing University · +1
  4. 2024PDF ↗
  5. 2024
    Low-rank Prompt Interaction for Continual Vision-Language RetrievalW. B. Yan, Ye Wang, Lin Wang … Tao JinACM International Conference on Multimedia · Zhejiang University
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  6. 2024
    Calibrating Prompt from History for Continual Vision-Language Retrieval and GroundingTao Jin, W. B. Yan, Ye Wang … Zhou ZhaoACM International Conference on Multimedia · Zhejiang University · Southeast University
  7. 2024
    Knowledge Adaptation Network for Few-Shot Class-Incremental LearningYe Wang, Yaxiong Wang, Guoshuai Zhao, Xueming QianarXiv
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  8. 2023
    Learning to complement: Relation complementation network for few-shot class-incremental learningYe Wang, Yaxiong Wang, Guoshuai Zhao, Xueming QianKnowledge-Based Systems · Hefei University of Technology · Xi'an Jiaotong University · +1
  9. 2023
    Improved Continually Evolved Classifiers for Few-Shot Class-Incremental LearningYe Wang, Guoshuai Zhao, Xueming QianIEEE TCSVT · Xi'an Jiaotong University
  10. 2023
    Knowledge Transfer-Driven Few-Shot Class-Incremental LearningYe Wang, Yaxiong Wang, Guoshuai Zhao, Xueming QianarXiv
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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. 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.