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Self-supervised pairwise-sample resistance model for few-shot classification

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Abstract

The traditional supervised learning models rely on high-quality labeled samples heavily. In many fields, training the model on limited labeled samples will result in a weak generalization ability of the model. To address this problem, we propose a novel few-shot image classification method by self-supervised and metric learning, which contains two training steps: (1) Training the feature extractor and projection head with strong representational ability by self-supervised technology; (2) taking the trained feature extractor and projection head as the initialization meta-learning model, and fine-tuning the meta-learning model by the proposed loss functions. Specifically, we construct the pairwise-sample meta loss (ML) to consider the influence of each sample on the target sample in the feature space, and propose a novel regularization technique named resistance regularization based on pairwise-samples which is utilized as an auxiliary loss in the meta-learning model. The model performance is evaluated on the 5-way 1-shot and 5-way 5-shot classification tasks of mini-ImageNet and tired-ImageNet. The results demonstrate that the proposed method achieves the state-of-the-art performance.

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Acknowledgements

This work was funded by the National Natural Science Foundation of China under Grant 51774219, Key R&D Projects in Hubei Province under grant 2020BAB098.

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Authors and Affiliations

  1. Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Weigang Li

  2. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Weigang Li, Lu Xie, Ping Gan & Yuntao Zhao

  3. Qunar, Beijing, 100080, Beijing, China

    Ping Gan

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  1. Weigang Li
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Correspondence to Ping Gan.

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Li, W., Xie, L., Gan, P. et al. Self-supervised pairwise-sample resistance model for few-shot classification. Appl Intell 53, 20661–20674 (2023). https://doi.org/10.1007/s10489-023-04525-4

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