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.

24 papers of 11,817Sort Recent · Most cited
  1. 2023
    Continual Learning: Forget-Free Winning Subnetworks for Video RepresentationsHaeyong Kang, Jaehong Yoon, Sung Ju Hwang, C. D. YooTPAMI
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  2. 2023
    Task-Distributionally Robust Data-Free Meta-LearningZixuan Hu, Yongxian Wei, Li Shen … D. TaoTPAMI
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  8. 2023
    Create Your World: Lifelong Text-to-Image DiffusionGan Sun, Wen-Qi Liang, Jiahua Dong … Yang CongTPAMI
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  9. 2023
    Continual Learning From a Stream of APIsEnneng Yang, Zhenyi Wang, Li Shen … Dacheng TaoTPAMI
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  10. 2023
    Incremental Learning for Simultaneous Augmentation of Feature and ClassChenping Hou, Shi-Lin Gu, Chao Xu, Yuhua QianTPAMI
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  13. 2023
    Advances and Challenges in Meta-Learning: A Technical ReviewAnna Vettoruzzo, Mohamed-Rafik Bouguelia, J. Vanschoren … K. SantoshTPAMI
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  14. 2023
    Learning Without Forgetting for Vision-Language ModelsDa-Wei Zhou, Yuanhan Zhang, Jingyi Ning … Ziwei LiuTPAMI
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  16. 2023
    Dual Compensation Residual Networks for Class Imbalanced LearningRui Hou, Hong Chang, Bingpeng Ma … Xilin ChenTPAMI
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  18. 2023
    Variational Data-Free Knowledge Distillation for Continual LearningXiaorong Li, Shipeng Wang, Jian Sun, Zongben XuTPAMI
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  20. 2023
    When Object Detection Meets Knowledge Distillation: A SurveyZhihui Li, Pengfei Xu, Xiaojun Chang … Xiaojiang ChenTPAMI
  21. 2023
    Class-Incremental Learning: A SurveyDa-Wei Zhou, Qiwen Wang, Zhiyuan Qi … Ziwei LiuTPAMI
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  22. 2023
    No One Left Behind: Real-World Federated Class-Incremental LearningJiahua Dong, Yang Cong, Gan Sun … Dengxin DaiTPAMI
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  23. 2023
    Continual Image Deraining With Hypergraph Convolutional NetworksXueyang Fu, Jie Xiao, Yurui Zhu … Zhengjun ZhaTPAMI
  24. 2023PDF ↗
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.