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.

20 papers of 8,653Sort Recent · Most cited
  1. 2025
    Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain NetworksJiaxing Miao, Liang Hu, Qi Zhang, Longbing CaoTPAMI · Tongji University · Macquarie University
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  2. 2025
    Model Rectification With Simultaneous Incremental Feature and Partial Label SetXijia Tang, Chao Xu, Chenping HouTPAMI · National University of Defense Technology
  3. 2025
    MoE-Adapters++: Toward More Efficient Continual Learning of Vision-Language Models Via Dynamic Mixture-of-Experts AdaptersJiazuo Yu, Zichen Huang, Yunzhi Zhuge … You HeTPAMI · Dalian University of Technology · University of Electronic Science and Technology of China · +2
  4. 2025
  5. 2025
    Replay Master: Automatic Sample Selection and Effective Memory Utilization for Continual Semantic SegmentationLanyun Zhu, Tianrun Chen, Jianxiong Yin … Jun LiuTPAMI · Singapore University of Technology and Design · Zhejiang University of Science and Technology · +1
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  6. 2025
  7. 2025
    PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental LearningFei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng‐Lin LiuTPAMI · Chinese University of Hong Kong, Shenzhen · Chinese Academy of Sciences · +1
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  8. 2025
    Continual Unsupervised Generative ModelingFei Ye, Adrian G. BorşTPAMI · University of Electronic Science and Technology of China · University of York
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  9. 2025
    HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient TuningLiyuan Wang, Jingyi Xie, Xingxing Zhang … Jun ZhuTPAMI · Tsinghua University
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  10. 2025
    Uncertainty-Calibrated Test-Time Model Adaptation Without ForgettingMingkui Tan, Guohao Chen, Jiaxiang Wu … Shuaicheng NiuTPAMI · South China University of Technology · National University of Singapore · +2
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  11. 2025
    Re-Fed+: A Better Replay Strategy for Federated Incremental LearningYichen Li, Haozhao Wang, Yining Qi … Ruixuan LiTPAMI · Huazhong University of Science and Technology
  12. 2025
    Replay Without Saving: Prototype Derivation and Distribution Rebalance for Class-Incremental Semantic SegmentationJ.R. Chen, Runmin Cong, Yuxuan Luo … Sam KwongTPAMI · City University of Hong Kong · Shandong University
  13. 2025
    Learning Without Forgetting for Vision-Language ModelsDa-Wei Zhou, Yuanhan Zhang, Yan Wang … Ziwei LiuTPAMI · Nanjing University · Nanyang Technological University
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  14. 2025
    Revisiting Flatness-Aware Optimization in Continual Learning With Orthogonal Gradient ProjectionEnneng Yang, Li Shen, Zhenyi Wang … Dacheng TaoTPAMI · Northeastern University · Sun Yat-sen University · +3
  15. 2025
    Few-Shot Class-Incremental Learning for Classification and Object Detection: A SurveyJinghua Zhang, Li Liu, Olli Sílven … Dewen HuTPAMI · National University of Defense Technology · University of Oulu
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  16. 2025
    Training Networks in Null Space of Feature Covariance With Self-Supervision for Incremental LearningShipeng Wang, Xiaorong Li, Jian Sun, Zongben XuTPAMI · Xi'an Jiaotong University
  17. 2025
    Continual Learning: Forget-Free Winning Subnetworks for Video RepresentationsHaeyong Kang, Jaehong Yoon, Sung Ju Hwang, Chang D. YooTPAMI · Korea Advanced Institute of Science and Technology
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  18. 2025
    A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual LearningZhenyi Wang, Enneng Yang, Li Shen, Heng HuangTPAMI · University of Maryland, College Park · Northeastern University · +1
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  19. 2025
    Language-Inspired Relation Transfer for Few-Shot Class-Incremental LearningYifan Zhao, Jia Li, Zeyin Song, Yonghong TianTPAMI · Beihang University · Peking University
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  20. 2025
    When Meta-Learning Meets Online and Continual Learning: A SurveyJaehyeon Son, Soochan Lee, Gunhee KimTPAMI · Seoul National University · LG (South Korea)
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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. 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.