@inproceedings{a2a71e946d7040efb91b6e30baa9179e,
title = "Out-of-distribution detection for fungi images with similar features",
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.",
keywords = "Attention branch network, Deep learning, Fungi, Out-of-distribution",
author = "Yutaka Kawashima and Mayuka Higo and Toshiyuki Tokiwa and Yukihiro Asami and Kenichi Nonaka and Yoshimitsu Aoki",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; 15th International Conference on Quality Control by Artificial Vision ; Conference date: 12-05-2021 Through 14-05-2021",
year = "2021",
doi = "10.1117/12.2591725",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Kenji Terada and Akio Nakamura and Takashi Komuro and Tsuyoshi Shimizu",
booktitle = "Fifteenth International Conference on Quality Control by Artificial Vision",
}