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.

10 papers of 8,653Sort Recent · Most cited
  1. 2026
    Continual Learning with Clip Text-Prototype and an Orthogonal Pre-Expanded Classification HeadTong Yu, Kanghao Chen, Jiantao Tan … Ruixuan WangICASSP · Sun Yat-sen University · Guangzhou University · +1
  2. 2026PDF ↗
  3. 2025
    Slowly expanding neural network for class incremental learningZhengjin Xu, Xuyang Li, Xiaobin Chang … Ruixuan WangPattern Recognition · Sun Yat-sen University · Key Laboratory of Guangdong Province · +2
  4. 2025
    LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual LearningXuan Liu, Xiaobin ChangCVPR · Sun Yat-sen University
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  5. 2024
    Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental LearningZhongren Yao, Xiaobin ChangChinese Conference on Pattern Recognition and Computer Vi…
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  6. 2024
    Consistent Prompting for Rehearsal-Free Continual LearningZhanxin Gao, Jun Cen, Xiaobin ChangCVPR · Sun Yat-sen University · Hong Kong University of Science and Technology · +1
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  7. 2024
    Generalizable Two-Branch Framework for Image Class-Incremental LearningChao Wu, Xiaobin Chang, Ruixuan WangICASSP · Sun Yat-sen University
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  8. 2023
    Dynamic Residual Classifier for Class Incremental LearningXiuwei Chen, Xiaobin ChangICCV · Sun Yat-sen University · Ministry of Education of the People's Republic of China · +1
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  9. 2023
    Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed AnalysisXiuwei Chen, Xiaobin ChangChinese Conference on Pattern Recognition and Computer Vi…
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  10. 2023
    SATS: Self-Attention Transfer for Continual Semantic SegmentationYiqiao Qiu, Yixing Shen, Zhuohao Sun … Ruixuan WangPattern Recognition · Sun Yat-sen University · Wuhan University · +2
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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.