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
    Cooperative meta-learning for incremental few-shot object detection in open urban environmentsYuan Li, C F Zhang, Song Yang … Lin WuPattern Recognition · Beijing Institute of Technology · Beijing Electronic Science and Technology Institute · +2
  2. 2026
    Cross Task Knowledge Transfer for Rehearsal-Free Continual LearningShuai Chen, Leike An, Jiaxi Wang … Jibin WangICASSP · China Mobile (China) · Beijing Institute of Technology
  3. 2026
    The Stability-Plasticity Dilemma Revisited: A Brain-Inspired Continual Learning Method with Representation-Function SeparationYuyang Han, Li Z, Zhiying Long, Xia WuICASSP · Beijing Normal University · Beijing Institute of Technology · +2
  4. 2026
    ReBac: Current-to-Past Residual Background Correction for Class-Incremental Semantic SegmentationGuangyu Gao, Anqi Zhang, Jianbo Jiao … Yunchao WeiIJCV · Beijing Institute of Technology · University of Birmingham · +1
  5. 2026
    Sculpting Margin Penalty: Intra-Task Adapter Merging and Classifier Calibration for Few-Shot Class-Incremental LearningLiang Bai, Hong Song, J. W. Li … Jian YangIEEE TCSVT · Beijing Institute of Technology
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  6. 2026
    Online label aggregation with incomplete crowd responsesYuyang Liu, Haoyu Liu, Runze Wu … Changjie FanInformation Sciences · Chinese Academy of Medical Sciences & Peking Union Medical College · NetEase (China) · +1
  7. 2026
    Toward Bidirectional Adaptability for Few-Shot Class-Incremental Learning With Forward-Backward Knowledge TransferBingzhi Chen, Zhiming Chen, Sudong Cai … Shengli XieIEEE Trans. Multimedia · Beijing Institute of Technology · Zhuhai Institute of Advanced 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. 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.