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. 2025
    CWPS: Efficient Channel-Wise Parameter Sharing for Knowledge TransferMingxuan Cui, Tao Wu, Xuewei Li … Xi LiTIP · Zhejiang University · Zhejiang University of Science and Technology · +2
  2. 2025
    E-CGL: an efficient continual graph learnerJianhao Guo, Zixuan Ni, Yun Zhu, Siliang TangFrontiers of Information Technology & Electronic Engineering · Zhejiang University of Science and Technology
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  3. 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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  4. 2025
    PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision TransformerQian Feng, Hanbin Zhao, Chao Zhang … Hui QianIEEE TCSVT · Zhejiang University of Science and Technology · Zhejiang University · +2
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  5. 2025
    CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary LearningYukun Li, Guansong Pang, Wei Suo … Peng WangTNNLS · Northwestern Polytechnical University · Singapore Management University · +2
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  6. 2025
    Class-wise federated unlearning: Harnessing active forgetting with teacher-student memory generationYuyuan Li, Jiaming Zhang, Yixiu Liu, Chaochao ChenKnowledge-Based Systems · Hangzhou Dianzi University · Zhejiang University of Science and Technology
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  7. 2025
    Metaplasticity‐Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks (Adv. Mater. 6/2025)Xuemeng Fan, Anzhe Chen, Zongwen Li … Yishu ZhangAdvanced Materials · Zhejiang University of Science and Technology · Zhejiang University · +1
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.