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Interpretable Convolutional Neural Network Including Attribute Estimation for Image Classification
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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