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.

5 papers of 8,653Sort Recent · Most cited
  1. 2024
    Create Your World: Lifelong Text-to-Image DiffusionGan Sun, Wenqi Liang, Jiahua Dong … Yang CongTPAMI · Shenyang Institute of Automation · Chinese Academy of Sciences · +5
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  2. 2023
    InOR-Net: Incremental 3-D Object Recognition Network for Point Cloud RepresentationJiahua Dong, Yang Cong, Gan Sun … Ender KonukoğluTNNLS · Shenyang Institute of Automation · Chinese Academy of Sciences · +4
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  3. 2023
    Class-Incremental Learning Based on Anomaly DetectionLijuan Zhang, Xiaokang Yang, Kai Zhang … Dongming LiIEEE Access · Changchun University of Technology · Jilin University of Finance and Economics · +1
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  4. 2022
    Few-shot class-incremental learning based on representation enhancementGuangle Yao, Juntao Zhu, Wenlong Zhou, Jun LiJournal of Electronic Imaging · Chengdu University of Technology · State Key Laboratory of Geohazard Prevention and Geoenvironment Protection
  5. 2018
    Robust Lifelong Multi-task Multi-view Representation LearningGan Sun, Yang Cong, Jun Li, Yun FuIEEE International Conference on Big Knowledge (ICBK) · University of Chinese Academy of Sciences · Shenyang Institute of Automation · +2
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.