Ryosuke Tanno

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Because of the recent progress on deep learning studies, Convolutional Neural Network (CNN) based method have outperformed conventional object recognition methods with a large margin. However, it requires much more memory and computational costs compared to the conventional methods. Therefore, it is not easy to implement a CNN-based object recognition(More)
Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for(More)
In this study, we create "Caffe2C" which converts CNN (Convolutional Neural Network) models trained with the existing CNN framework, Caffe, C-language source codes for mobile devices. Since Caffe2C generates a single C code which includes everything needed to execute the trained CNN, csCaffe2C makes it easy to run CNN-based applications on any kinds of(More)
In this paper, we propose a conditional fast neural style transfer network. We extend the network proposed as a fast neural style transfer network by Johnson et al. [1] so that the network can learn multiple styles at the same time. To do that, we add a conditional input which selects a style to be transferred out of the trained styles. In addition, we show(More)
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