Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 5,456Sort Recent · Most cited
  1. 2023
    Continual Learning via Manifold Expansion ReplayZihao Xu, Xuan Tang, Yufei Shi … Xian WeiIEEE International Conference on Systems, Man, and Cybern… · East China Normal University · Sun Yat-sen University · +2
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  2. 2023
    Instance and Category Supervision are Alternate Learners for Continual LearningXudong Tian, Zhizhong Zhang, Xin Tan … Yuan XieICCV · Chongqing Normal University · Shanghai Key Laboratory of Computer Software Testing and Evaluating · +3
  3. 2023
    AttriCLIP: A Non-Incremental Learner for Incremental Knowledge LearningRunqi Wang, Xiaoyue Duan, Guoliang Kang … Baochang ZhangCVPR · Huawei Technologies (Sweden) · Beihang University · +1
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  4. 2023
    GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic ForgettingKangyang Luo, Xiang Li, Yunshi Lan, Ming GaoCVPR · East China Normal University
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  5. 2023
    Rethinking Gradient Projection Continual Learning: Stability/Plasticity Feature Space DecouplingZhen Zhao, Zhizhong Zhang, Xin Tan … Lizhuang MaCVPR · East China Normal University · Tencent (China) · +1
  6. 2023
    Multi-Centroid Task Descriptor for Dynamic Class Incremental InferenceTenghao Cai, Zhizhong Zhang, Xin Tan … Yuan XieCVPR · East China Normal University · Xiamen University · +1
  7. 2023
    Uncertainty-Aware Few-Shot Class-Incremental LearningJiancai Zhu, Jiabao Zhao, Jiayi Zhou … Zhi ZhangICASSP · East China Normal University · New York University Shanghai · +1
  8. 2023
    Rehearsal-free Continual Language Learning via Efficient Parameter IsolationZhicheng Wang, Yufang Liu, Tao Ji … Wenqiu ZengACL · East China Normal University · China Institute of Finance and Capital Markets
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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. It lists only 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. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.