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.

9 papers of 8,653Sort Recent · Most cited
  1. 2025
    Incremental Object Keypoint LearningMingfu Liang, Jiahuan Zhou, Xu Zou, Ying WuCVPR · Peking University · Huazhong University of Science and Technology
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  2. 2024
    CLOVA: A Closed-LOop Visual Assistant with Tool Usage and UpdateZhi Gao, Yuntao Du, Xintong Zhang … Qing LiCVPR · King University · Peking University · +2
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  3. 2024
    FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental LearningQiwei Li, Yuxin Peng, Jiahuan ZhouCVPR · Peking University
  4. 2024
    Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time AdaptationJiaming Liu, Ran Xu, Senqiao Yang … Shanghang ZhangCVPR · Peking University · University of Hong Kong · +2
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  5. 2024
    Solving the Catastrophic Forgetting Problem in Generalized Category DiscoveryXinzi Cao, Xiawu Zheng, Guanhong Wang … Yonghong TianCVPR · Sun Yat-sen University · Peng Cheng Laboratory · +2
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  6. 2023
    Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental LearningZeyin Song, Yifan Zhao, Yujun Shi … Yonghong TianCVPR · Peking University · National University of Singapore · +1
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  7. 2023
    Regularizing Second-Order Influences for Continual LearningZhicheng Sun, Yadong Mu, Gang HuaCVPR · Peking University · Peng Cheng Laboratory
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  8. 2023
    Dealing with Cross-Task Class Discrimination in Online Continual LearningYiduo Guo, Bing Liu, Dongyan ZhaoCVPR · Peking University · University of Illinois Chicago
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  9. 2022
    Alleviating Representational Shift for Continual Fine-tuningShibo Jie, Zhihong Deng, Ziheng LiCVPR · Peking University · Artificial Intelligence in Medicine (Canada)
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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.