[Papers] Interpretable Convolutional Neural Network Including Attribute Estimation for Image Classification
Kazaha Horii, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · ITE Transactions on Media Technology and Applications · 2020
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.