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.

9 papers of 8,653Sort 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. 2024PDF ↗
  4. 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
  5. 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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  6. 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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  7. 2024
    Analogical Learning-Based Few-Shot Class-Incremental LearningJiashuo Li, Songlin Dong, Yihong Gong … Xing WeiIEEE TCSVT · Xi'an Jiaotong University
  8. 2024
    Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental LearningXiang Song, Kuang Shu, Songlin Dong … Yihong GongWACV · Xi'an Jiaotong University · China Power Engineering Consulting Group (China) · +1
  9. 2024
    Deep Class-Incremental Learning From Decentralized DataXiaohan Zhang, Songlin Dong, Jinjie Chen … Xiaopeng HongTNNLS · Xi'an Jiaotong University · Huawei Technologies (China) · +1
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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.