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Multi-distance metric network for few-shot learning

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Abstract

Few-shot learning aims to make classification when few samples are available. In general, metric-based methods map images into a space by learning the embedding function. However, conventional metric-based methods rely on a single distance value, which does not pay attention to the shallow features. In this paper, we propose a multi-distance metric network (MDM-Net) by employing a multi-output embedding network to map samples into different feature spaces. In addition, we maximize the inter-class distance which is popular in metric learning field to improve the performance of few-shot classifier. Furthermore, we design a task-adaptive margin to adjust the distance between different sample pairs, and we found that the distance loss combined with cross-entropy loss is beneficial to achieve better results in meta-task training. The proposed method is verified by tests on miniImageNet and FC100 these two benchmarks for 5-way 1-shot classification task and 5-way 5-shot classification task with competitive results.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018AAA0101601), and the Zhejiang Provincial Natural Science Foundation of China (LY20E050011).

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Farong Gao, Lijie Cai & Zhangyi Yang

  2. Department of Automation, Tsinghua University, Beijing, 100084, China

    Shiji Song & Cheng Wu

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  1. Farong Gao
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Gao, F., Cai, L., Yang, Z. et al. Multi-distance metric network for few-shot learning. Int. J. Mach. Learn. & Cyber. 13, 2495–2506 (2022). https://doi.org/10.1007/s13042-022-01539-1

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