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Recent advances of few-shot learning methods and applications

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Abstract

The rapid development of deep learning provides great convenience for production and life. However, the massive labels required for training models limits further development. Few-shot learning which can obtain a high-performance model by learning few samples in new tasks, providing a solution for many scenarios that lack samples. This paper summarizes few-shot learning algorithms in recent years and proposes a taxonomy. Firstly, we introduce the few-shot learning task and its significance. Secondly, according to different implementation strategies, few-shot learning methods in recent years are divided into five categories, including data augmentation-based methods, metric learning-based methods, parameter optimization-based methods, external memory-based methods, and other approaches. Next, We investigate the application of few-shot learning methods and summarize them from three directions, including computer vision, human-machine language interaction, and robot actions. Finally, we analyze the existing few-shot learning methods by comparing evaluation results on miniImageNet, and summarize the whole paper.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    JianYuan Wang, KeXin Liu & JinHu Lu

  2. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    YuCheng Zhang & Biao Leng

Authors
  1. JianYuan Wang
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  2. KeXin Liu
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  3. YuCheng Zhang
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  4. Biao Leng
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  5. JinHu Lu
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Corresponding author

Correspondence to KeXin Liu.

Additional information

This work was supported by the National Key R&D Program of China (Grant No. 2019YFB2102400), and the National Natural Science Foundation of China (Grant No. 92067204).

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Wang, J., Liu, K., Zhang, Y. et al. Recent advances of few-shot learning methods and applications. Sci. China Technol. Sci. 66, 920–944 (2023). https://doi.org/10.1007/s11431-022-2133-1

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  • DOI: https://doi.org/10.1007/s11431-022-2133-1

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