2024-03-29T15:42:42Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/781342022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Interpretable Convolutional Neural Network Including Attribute Estimation for Image ClassificationHorii, KazahaMaeda, KeisukeOgawa, TakahiroHaseyama, MikiInterpretable convolutional neural networkattribute estimationimage classification540An 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.The Institute of Image Information and Television EngineersJournal Articlehttp://hdl.handle.net/2115/781342186-7364ITE Transactions on Media Technology and Applications821111242020enginfo:doi/10.3169/mta.8.111none