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One-Shot Image Learning Using Test-Time Augmentation

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13188))

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Abstract

Modern image recognition systems require a large amount of training data. In contrast, humans can learn the concept of new classes from only one or a few image examples. A machine learning problem with only a few training samples is called few-shot learning and is a key challenge in the image recognition field. In this paper, we address one-shot learning, which is a type of few-shot learning in which there is one training sample per class. We propose a one-shot learning method based on metric learning that is characterized by data augmentation of a test target along with the training samples. Experimental results demonstrate that expanding both training samples and test target is effective in terms of improving accuracy. On a benchmark dataset, the accuracy improvement by the proposed method is 2.55% points, while the improvement by usual data augmentation which expands the training samples is 1.31% points. Although the proposed method is very simple, it achieves accuracy that is comparable or superior to some of existing methods.

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Correspondence to Keiichi Yamada .

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Yamada, K., Matsumi, S. (2022). One-Shot Image Learning Using Test-Time Augmentation. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_1

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  • DOI: https://doi.org/10.1007/978-3-031-02375-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02374-3

  • Online ISBN: 978-3-031-02375-0

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