Out-of-distribution detection for fungi images with similar features

Yutaka Kawashima; Mayuka Higo; Toshiyuki Tokiwa; Yukihiro Asami; Kenichi Nonaka; Yoshimitsu Aoki

doi:10.1117/12.2591725

16 July 2021 Out-of-distribution detection for fungi images with similar features

Yutaka Kawashima, Mayuka Higo, Toshiyuki Tokiwa, Yukihiro Asami, Kenichi Nonaka, Yoshimitsu Aoki

Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision; 117941E (2021) https://doi.org/10.1117/12.2591725
Event: Fifteenth International Conference on Quality Control by Artificial Vision, 2021, Tokushima, Japan

Abstract

In order to create a classification model for fungi, it is necessary to have robustness against out-of-distribution data from the viewpoint of practicality. Therefore, in this paper, we perform out-of-distribution detection on a fungi. Unlike the case of conventional out-of-distribution detection, the characteristics of in-distribution data and out-of-distribution data in this paper are very similar. Therefore, the problem in which conventional methods using out-of-distribution data for validation are not effective is mentioned. We also verify whether the accuracy of out-of-distribution detection can be improved using the attention branch network.

Citation Download Citation

Yutaka Kawashima, Mayuka Higo, Toshiyuki Tokiwa, Yukihiro Asami, Kenichi Nonaka, and Yoshimitsu Aoki "Out-of-distribution detection for fungi images with similar features", Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 117941E (16 July 2021); https://doi.org/10.1117/12.2591725

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