IEICE TRANSACTIONS on Fundamentals
Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation
Summary :
A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.12 pp.1609-1612
- Publication Date
- 2020/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020SML0006
- Type of Manuscript
- Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
- Category
- Neural Networks and Bioengineering
Authors
Keisuke MAEDA
Hokkaido University
Kazaha HORII
Hokkaido University
Takahiro OGAWA
Hokkaido University
Miki HASEYAMA
Hokkaido University
Keyword
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Keisuke MAEDA, Kazaha HORII, Takahiro OGAWA, Miki HASEYAMA, "Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1609-1612, December 2020, doi: 10.1587/transfun.2020SML0006.
Abstract: A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020SML0006/_p
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@ARTICLE{e103-a_12_1609,
author={Keisuke MAEDA, Kazaha HORII, Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation},
year={2020},
volume={E103-A},
number={12},
pages={1609-1612},
abstract={A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.},
keywords={},
doi={10.1587/transfun.2020SML0006},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1609
EP - 1612
AU - Keisuke MAEDA
AU - Kazaha HORII
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2020
DO - 10.1587/transfun.2020SML0006
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.
ER -
English
