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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    Boosting Few-Shot Continual Learning via Self-Adaptive EvolutionZiqi Gu, Chunyan Xu, Yue Wang … Zhen CuiTIP · Nanjing University of Science and Technology · Nanyang Technological University · +2
  2. 2024
    MR-GDINO: Efficient Open-World Continual Object DetectionBowen Dong, Zitong Huang, Guanglei Yang … Wangmeng ZuoarXiv
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  3. 2024PDF ↗
  4. 2024
    ConSept: Continual Semantic Segmentation via Adapter-based Vision TransformerBowen Dong, Guanglei Yang, Wangmeng Zuo, Lei ZhangarXiv
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  5. 2023
    Spatial–Temporal Federated Learning for Lifelong Person Re-Identification on Distributed EdgesLei Zhang, Guanyu Gao, Huaizheng ZhangIEEE TCSVT · Nanjing University of Science and Technology · Nanyang Technological University
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  6. 2022
    Improve the Performance and Stability of Incremental Learning by a Similarity Harmonizing MechanismJing Ma, Mingjie Liao, Lei ZhangIEEE Access · Beijing Normal University · Shanghai Jiao Tong University
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  7. 2019
    Learning Continually from Low-shot Data StreamCanyu Le, Xihan Wei, Biao Wang … Chen, ZhongguiarXiv
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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.