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.

15 papers of 8,653Sort Recent · Most cited
  1. 2026
    Robust adaptation of visual representations for few-shot class-incremental learning via geometric calibrationTao Zhang, C.L. Philip Chen, Xiwen Luo … Jiehao LiPattern Recognition · South China University of Technology · South China Agricultural University · +1
  2. 2026
    Learning to Replay: A meta-reinforcement learning approach to experience replay in continual learningHaoran Yu, Jianzhou Feng, Huaxiao Qiu … Tianyu YangPattern Recognition · Yanshan University · Hebei University of Environmental Engineering
  3. 2026
    Continual learning via semantic memory systemMingsen Luo, Qihe Liu, Fei Ye … Shijie ZhouPattern Recognition · University of Electronic Science and Technology of China · University of York
  4. 2026
    Compact feature distributions and refined pseudo labels for weakly supervised incremental semantic segmentationZilin Guo, Dongyue Wu, Changxin Gao, Nong SangPattern Recognition · Huazhong University of Science and Technology
  5. 2026
  6. 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
  7. 2026
  8. 2026
    CSBoRA: A continual learning method for large language models with true orthogonality and reduced forgettingYuyang Liu, Lai-Man Po, Farrell Hung … Kwok-Wai CheungPattern Recognition · City University of Hong Kong · nLIGHT (United States) · +2
  9. 2026
    MIP: Mutual information-guided prompt for class-incremental continual graph learningQiao Yuan, Boxuan Zhu, Weizhi Huang … Jieming MaPattern Recognition · University of Liverpool · Xi’an Jiaotong-Liverpool University
  10. 2026
    Incomplete cross-modality class-incremental learning in visible-thermal recognitionXinjie Yao, Yanxian Bi, Yu Wang … Qinghua HuPattern Recognition · Kunming University of Science and Technology · Tianjin University · +5
  11. 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
  12. 2026
    Learning task-shared and specific knowledge via mixture-of-experts in generative model for continual learningWeinan Zhao, Yanling Ji, Yan Li … Dingwen ZhangPattern Recognition · Northwestern Polytechnical University · Xidian University · +1
  13. 2026
    PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental LearningXianghu Yue, Yiming Chen, Xueyi Zhang … Haizhou LiPattern Recognition · Tianjin University · National University of Singapore · +4
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  14. 2026
    CASP: Few-Shot Class-Incremental Learning with CLS Token Attention Steering PromptsShuai Huang, Xuhan Lin, Yuwu LuPattern Recognition · South China Normal University
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  15. 2026
    PSR: Proactive soft-orthogonal regulation for long-tailed class-incremental learningZhihan Fu, Zhiqi Zhang, Shipeng Liao … Tianyu ShenPattern Recognition · Beijing University of Chemical Technology · Beijing Information Science & Technology University
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.