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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    Continual Learning across multiple domains via a Dynamic Expandable and Mergeable ModelFei Ye, Ruilong Yu, Qihe Liu … Kun ZhangEng. Applications of AI · University of Electronic Science and Technology of China · University of York · +2
  2. 2026
    Text-guided class-incremental point cloud semantic segmentation with category distribution constraintChao Zheng, Yan Xu, Xiaorui Peng … Yu MengEng. Applications of AI · University of Science and Technology Beijing
  3. 2024
    Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approachPablo García-Santaclara, Bruno Fernández-Castro, Rebeca P. Dı́az RedondoEng. Applications of AI
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  4. 2024
    Semi-supervised few-shot class-incremental learning based on dynamic topology evolutionWenqi Han, Kai Huang, Jie Geng, Wen JiangEng. Applications of AI · Northwestern Polytechnical University
  5. 2023
    Continual learning classification method with human-in-the-loopJia Liu, Dong Li, Wangweiyi Shan, Shulin LiuEng. Applications of AI
  6. 2022
    A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment MonitoringDavide Dalle Pezze, Denis Deronjic, Chiara Masiero … Gian Antonio SustoEng. Applications of AI
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  7. 2022
    Online Continual Learning via the Meta-learning update with Multi-scale Knowledge Distillation and Data AugmentationYa-nan Han, Jian–wei LiuEng. Applications of AI · China University of Petroleum, Beijing
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  8. 2020
    Reinforcement learning for quadrupedal locomotion with design of continual-hierarchical curriculumTaisuke Kobayashi, Toshiki SuginoEng. Applications of AI · Nara Institute of Science and Technology
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.