Journal of the Japan Society for Precision Engineering
Online ISSN : 1882-675X
Print ISSN : 0912-0289
ISSN-L : 0912-0289
Few-Shot Domain Adaptation For Many Class Classification Using Commercial Products
Ryo TakahashiYuji SatoJunko FuruyamaMegumi YamaokaMasamoto TanabikiYoshimitsu Aoki
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2021 Volume 87 Issue 1 Pages 78-82


In order to train a classifier for self checkout system in convenience store at low cost, a few-shot domain adaptation problem has to be solved. Since the system treats a classifier for large number of classes, conventional method of few-shot domain adaptation should be extended for many classes. This paper propose to exploit meta-class information by executing the adaptation on the normal-class level and the meta-class level simultaneously. The proposed method are shown to be effective for improving adaptation accuracy of a classifier for many classes. The results of our ablation study implies that i) the meta-class should be decided by using k-means clustering method rather than clustering manually, and that ii) the ratio between the number of normal-class and the number of meta-class should be fixed.

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