An architecture design method of deep convolutional neural network

Abstract

Deep Convolutional Neural Network (DCNN) is a kind of multi layer neural network models. In these years, the DCNN is attracting the attention since it shows the state-of-the-arts performance in the image and speech recognition tasks. However, the design for the architecture of the DCNN has not so much discussed since we have not found effective guideline to construct. In this research, we focus on within-class variance of SVM histogram proposed in our previous work [8]. We try to apply it as a clue for modifying the architecture of a DCNN, and confirm the modified DCNN shows better performance than that of the original one.