Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 6,984Sort Recent · Most cited
  1. 2024
    CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental LearningSonglin Dong, Xinyuan Gao, Yuhang He … Yihong GongIEEE TCSVT · Xi'an Jiaotong University · Nanyang Technological University
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  2. 2024
    Non-exemplar Domain Incremental Learning via Cross-Domain Concept IntegrationQiang Wang, Yuhang He, Songlin Dong … Yihong GongECCV · Xi'an Jiaotong University
  3. 2024
    DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental LearningYuhang He, Yingjie Chen, Yuhan Jin … Yihong GongCVPR · Xi'an Jiaotong University
  4. 2024
    Evolving Parameterized Prompt Memory for Continual LearningMuhammad Rifki Kurniawan, Xiang Song, Zhiheng Ma … Xing WeiAAAI · Xi'an Jiaotong University · Chinese Academy of Sciences · +1
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  5. 2024
    Non-exemplar Domain Incremental Object Detection via Learning Domain BiasXiang Song, Yuhang He, Songlin Dong, Yihong GongAAAI · Xi'an Jiaotong University
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  6. 2024
    Analogical Learning-Based Few-Shot Class-Incremental LearningJiashuo Li, Songlin Dong, Yihong Gong … Xing WeiIEEE TCSVT · Xi'an Jiaotong University
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.