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. 2026
  2. 2026
    Estimating Representation Drift for Prompt-Based Class-Incremental LearningYuting Hou, Rongyu Zhu, Junjie Liu, Kedian MuSpringer LNCS · Peking University
  3. 2026
    AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental LearningPengxiang Wang, Hongbo Bo, Jun Hong … Kedian MuSpringer LNCS · Peking University · University of Bristol · +2
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  4. 2024
    Disentangled Representations for Continual Learning: Overcoming Forgetting and Facilitating Knowledge TransferZhaopeng Xu, Qi Qin, Bing Liu, Dongyan ZhaoSpringer LNCS · Peking University · University of Illinois Chicago · +1
  5. 2023
    Recent Advances in Class-Incremental LearningDejie Yang, Minghang Zheng, Weishuai Wang … Yang LiuSpringer LNCS · Peking University
  6. 2023
    Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANsZeren Zhang, Xingjian Li, Hong Tao … Chengzhong XuSpringer LNCS · Peking University · Baidu (China) · +2
  7. 2022
    Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action LocalizationTianquan Chen, Bairong Li, Yusheng Tao … Yuesheng ZhuSpringer LNCS · Peking University
  8. 2022
    Balancing Between Forgetting and Acquisition in Incremental Subpopulation LearningMingfu Liang, Jiahuan Zhou, Wei Wei, Ying WuSpringer LNCS · Northwestern University · Peking University
  9. 2021
    Principal Gradient Direction and Confidence Reservoir Sampling for Continual LearningZhiyi Chen, Tong LinSpringer LNCS · Georgia Institute of Technology · Peking University · +1
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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 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.