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.

6 papers of 8,653Sort Recent · Most cited
  1. 2024
    Dual prototypes for adaptive pre-trained model in class-incremental learningZhiming Xu, Zhiming Xu, Suorong Yang … Jian ZhaoNeural Networks · Nanjing University · Nanjing University of Science and Technology
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  2. 2024
    TMM-CLIP: Task-guided Multi-Modal Alignment for Rehearsal-Free Class Incremental LearningYuankang Pan, Zhaoquan Yuan, Xiao Wu … Changsheng XuACM International Conference on Multimedia in Asia · Southwest Jiaotong University · Nanjing University of Science and Technology · +1
  3. 2024
    Human Motion Forecasting in Dynamic Domain Shifts: A Homeostatic Continual Test-Time Adaptation FrameworkQiongjie Cui, Huaijiang Sun, Weiqing Li … Bin LiSpringer LNCS · Nanjing University of Science and Technology
  4. 2024
    Continual Learning meets Multimodal Foundation Models: Fundamentals and AdvancesWenbin Li, Qi Fan, Rui Yan … Jiebo Luoon Continual Learning meets Multimodal Foundation Models:… · Nanjing University · Systems Engineering Society of China · +3
  5. 2024
    Continual Multi-View Clustering with Consistent Anchor GuidanceWei Hua, Chenlin Zhou, Jibin Wu … Yangyang ShuIJCAI · Nanjing University · University of Chinese Academy of Sciences · +3
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  6. 2024
    Dynamic Replay Training for Class-Incremental LearningYan Yang, Dongdong Ren, Chenglei Peng … Yang GaoICASSP · Nanjing University · Nanjing University of Science and Technology
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.