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 language | English |
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Pages (from-to) | 961-964 |
Number of pages | 4 |
Journal | Proceedings of the International Display Workshops |
Volume | 27 |
Publication status | Published - 2021 Dec 9 |
Event | 27th International Display Workshops, IDW 2020 - Virtual, Online Duration: 2020 Dec 9 → 2020 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