On generalization of deep neural networks for visual recognition tasks

Research output: Contribution to journalConference articlepeer-review

Abstract

This article discusses generalization ability of deep neural networks (DNNs) for visual recognition. It is known that DNNs easily fail for images to which noises are added, when they have not learned the noisy images. We discuss how to cope with such limitation of DNNs.

Original languageEnglish
Pages (from-to)961-964
Number of pages4
JournalProceedings of the International Display Workshops
Volume27
Publication statusPublished - 2021 Dec 9
Event27th International Display Workshops, IDW 2020 - Virtual, Online
Duration: 2020 Dec 92020 Dec 11

Keywords

  • Computer vision
  • Deep learning
  • Image restoration
  • Out-of-distribution detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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