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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルFifteenth International Conference on Quality Control by Artificial Vision
編集者Kenji Terada, Akio Nakamura, Takashi Komuro, Tsuyoshi Shimizu
出版社SPIE
ISBN(電子版)9781510644267
DOI
出版ステータスPublished - 2021
イベント15th International Conference on Quality Control by Artificial Vision - Tokushima, Virtual, Japan
継続期間: 2021 5月 122021 5月 14

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11794
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Conference

Conference15th International Conference on Quality Control by Artificial Vision
国/地域Japan
CityTokushima, Virtual
Period21/5/1221/5/14

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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