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Authors: Daiju Kanaoka 1 ; Yuichiro Tanaka 2 and Hakaru Tamukoh 1 ; 2

Affiliations: 1 Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, Japan ; 2 Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, Japan

Keyword(s): Open-set Recognition, Neural Networks, Image Classification, Unknown Class.

Abstract: With the advent of deep learning, significant improvements in image recognition performance have been achieved. In image recognition, it is generally assumed that all the test data are composed of known classes. This approach is termed as closed-set recognition. In closed-set recognition, when an untrained, unknown class is input, it is recognized as one of the trained classes. The method whereby an unknown image is recognized as unknown when it is input is termed as open-set recognition. Although several open-set recognition methods have been proposed, none of these previous methods excel in terms of all three evaluation items: learning cost, recognition performance, and scalability from closed-set recognition models. To address this, we propose an open-set recognition method using the distance between features in the multidimensional feature space of neural networks. By applying center loss to the feature space, we aim to maintain the classification accuracy of closed-set recogniti on and improve the unknown detection performance. In our experiments, we achieved state-of-the-art performance on the MNIST, SVHN, and CIFAR-10 datasets. In addition, the proposed approach shows excellent performance in terms of the three evaluation items. (More)

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Paper citation in several formats:
Kanaoka, D.; Tanaka, Y. and Tamukoh, H. (2022). Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 359-365. DOI: 10.5220/0010816600003124

@conference{visapp22,
author={Daiju Kanaoka. and Yuichiro Tanaka. and Hakaru Tamukoh.},
title={Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={359-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010816600003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-set Recognition
SN - 978-989-758-555-5
IS - 2184-4321
AU - Kanaoka, D.
AU - Tanaka, Y.
AU - Tamukoh, H.
PY - 2022
SP - 359
EP - 365
DO - 10.5220/0010816600003124
PB - SciTePress