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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationFifteenth International Conference on Quality Control by Artificial Vision
EditorsKenji Terada, Akio Nakamura, Takashi Komuro, Tsuyoshi Shimizu
ISBN (Electronic)9781510644267
Publication statusPublished - 2021
Event15th International Conference on Quality Control by Artificial Vision - Tokushima, Virtual, Japan
Duration: 2021 May 122021 May 14

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference15th International Conference on Quality Control by Artificial Vision
CityTokushima, Virtual


  • Attention branch network
  • Deep learning
  • Fungi
  • Out-of-distribution

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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