Fast training of adaptive structural learning method of deep learning for multi modal data
by Shin Kamada; Takumi Ichimura
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 7, No. 3/4, 2018

Abstract: Recently, deep learning has been applied in the techniques of artificial intelligence. Especially, their new architectures performed good results in the field of image recognition. However, the method is required to train not only image data, but also numerical data, text data, and other binary data. Multi modal data consists of two or more kinds of data such as a pair of image and text of giving an explanation of the image. The arrangement of multi modal data in the traditional method is formed in the squared array with no specification. In this paper, the method can modify the squared array of the multi modal data, according to the similarity of input-output pattern of adaptive structural learning method of deep belief network. Some experimental results show that the computational time of deep learning decreases.

Online publication date: Thu, 15-Nov-2018

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