Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2025
    Sequence Transferability and Task Order Selection in Continual LearningThinh T. H. Nguyen, Cuong N. Nguyen, Quang Pham … Cuong V NguyenarXiv
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  2. 2023
    COCO-TEACH: A Contrastive Co-Teaching Network For Incremental 3D Object DetectionZhongyao Cheng, Cen Chen, Ziyuan Zhao … Xulei YangInternational Conference on Information Photonics
  3. 2021
    DO-GAN: A Double Oracle Framework for Generative Adversarial NetworksAye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu … Xiaoli LiCVPR · Agency for Science, Technology and Research · Nanyang Technological University · +1
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  4. 2021
    SGDOL: Self-evolving Generative and Discriminative Online Learning for Data Stream ClassificationDeeksha Aggarwal, J. Senthilnath, Uttam Kumar … Xiaoli LiICDM · International Institute of Information Technology Bangalore · Agency for Science, Technology and Research · +2
  5. 2015
    TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text ClusteringLinmei Hu, Juanzi Li, Xiaoli Li … Xuzhong WangEMNLP · Institute for Infocomm Research · Tsinghua University · +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. 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.