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Interpretable Convolutional Neural Network Including Attribute Estimation for Image Classification

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Title: Interpretable Convolutional Neural Network Including Attribute Estimation for Image Classification
Authors: Horii, Kazaha Browse this author
Maeda, Keisuke Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Interpretable convolutional neural network
attribute estimation
image classification
Issue Date: 2020
Publisher: The Institute of Image Information and Television Engineers
Journal Title: ITE Transactions on Media Technology and Applications
Volume: 8
Issue: 2
Start Page: 111
End Page: 124
Publisher DOI: 10.3169/mta.8.111
Abstract: An interpretable convolutional neural network (CNN) including attribute estimation for image classification is presented in this paper. Although CNNs perform highly accurate image classification, the reason for the classification results obtained by the neural networks is not clear. In order to provide interpretation of CNNs, the proposed method estimates attributes, which explain elements of objects, in an intermediate layer of the network. This enables improvement of the interpretability of CNNs, and it is the main contribution of this paper. Furthermore, the proposed method uses the estimated attributes for image classification in order to enhance its accuracy. Consequently, the proposed method not only provides interpretation of CNNs but also realizes improvement in the performance of image classification.
Type: article
URI: http://hdl.handle.net/2115/78134
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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