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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    Towards continual low-light image enhancement through causal inferenceFan Ji, Hao Li, Jiangmeng Li … Fanjiang XuNeural Networks · Institute of Software · Aerospace Information Research Institute · +1
  2. 2025
    C$^2$Prompt: Class-aware Client Knowledge Interaction for Federated Continual LearningKunlun Xu, Yibo Feng, Jiangmeng Li … Jiahuan ZhouNeurIPS · Peking University · University of Electronic Science and Technology of China · +3
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  3. 2024
    TaSL: Continual Dialog State Tracking via Task Skill Localization and ConsolidationYujie Feng, Xu Chu, Yongxin Xu … Xiao-Ming WuACL · Hong Kong Polytechnic University · Peking University · +1
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  4. 2024
    CTL-I: Infrared Few-Shot Learning via Omnidirectional Compatible Class-IncrementalBiwen Yang, Ruiheng Zhang, Yumeng Liu … Lixin XuLecture notes of the Institute for Computer Sciences, Soc… · Beijing Institute of Technology · Chinese Academy of Sciences · +1
  5. 2023
    New Insights on Relieving Task-Recency Bias for Online Class Incremental LearningGuoqiang Liang, Zhaojie Chen, Zhaoqiang Chen … Yanning ZhangIEEE TCSVT · Northwestern Polytechnical University · Institute of Software
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  6. 2023
    CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the InternetMiaomiao Li, Jiaqi Zhu, Xin Yang … Hongan WangACM Web Conference 2023 · Institute of Software · University of Chinese Academy of Sciences · +2
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  7. 2023
    Class-incremental object detectionNa Dong, Yongqiang Zhang, Mingli Ding, Yancheng BaiPattern Recognition · Harbin Institute of Technology · Chinese Academy of Sciences · +1
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.