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.

8 papers of 11,817Sort Recent · Most cited
  1. 2024PDF ↗
  2. 2024PDF ↗
  3. 2024
    Sharpness-aware gradient guidance for few-shot class-incremental learningRun-Hang Chen, Xiao-Yuan Jing, Fei Wu, Haowen ChenKnowledge-Based Systems
  4. 2023
    Task-specific parameter decoupling for class incremental learningRun-Hang Chen, Xiao-Yuan Jing, Fei Wu … Yaru HaoInformation Sciences
  5. 2022
    E2-AEN: End-to-End Incremental Learning with Adaptively Expandable NetworkGuimei Cao, Zhanzhan Cheng, Yunlu Xu … Fei WuarXiv
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  6. 2020
    MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental LearningHanbin Zhao, Yongjian Fu, Mintong Kang … Xi LiTPAMI · Zhejiang University of Science and Technology · Huawei Technologies (China)
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  7. 2020
    Memory-Efficient Class-Incremental Learning for Image ClassificationHanbin Zhao, Hui Wang, Yongjian Fu … Xi LiTNNLS · Zhejiang University of Science and Technology · Shanghai Advanced Research Institute
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  8. 2019
    RBER-Aware Lifetime Prediction Scheme for 3D-TLC NAND Flash MemoryRuixiang Ma, Fei Wu, Meng Zhang … Changsheng XieIEEE Access · Wuhan National Laboratory for Optoelectronics · Huazhong University of Science and Technology · +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.