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Knowledge transfer based hierarchical few-shot learning via tree-structured knowledge graph

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Abstract

Few-shot learning poses a great challenge for obtaining a classifier that recognizes new classes from a few labeled examples. Existing solutions perform well by leveraging meta-learning models driven by data information. However, these models only utilize the flat data information and ignore the existing hierarchical knowledge structure among classes. In this paper, we propose a knowledge transfer based hierarchical few-shot learning model, which takes advantage of a tree-structured knowledge graph to facilitate the classification results. First, we consider a tree-structured class hierarchy according to the semantic information among classes as a knowledge graph to alleviate the low-data problem. Second, we divide the tree structure into class structure and data, and build a multi-layer classifier to obtain classification results in the two parts. Finally, we consider the tradeoff between structure loss and data loss for hierarchical few-shot learning, which takes class structure information to assist learning. Experimental results on benchmark datasets show that our model outperforms several state-of-the-art models.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62141602 and the Natural Science Foundation of Fujian Province under Grant Nos. 2021J011003 and 2021J011006.

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China

    Zhong Zhang, Zhiping Wu, Hong Zhao & Minjie Hu

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou, 363000, Fujian, China

    Zhong Zhang, Zhiping Wu, Hong Zhao & Minjie Hu

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  1. Zhong Zhang
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  2. Zhiping Wu
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  3. Hong Zhao
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  4. Minjie Hu
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Correspondence to Hong Zhao.

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Zhang, Z., Wu, Z., Zhao, H. et al. Knowledge transfer based hierarchical few-shot learning via tree-structured knowledge graph. Int. J. Mach. Learn. & Cyber. 14, 281–294 (2023). https://doi.org/10.1007/s13042-022-01640-5

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