Title: Fast training of adaptive structural learning method of deep learning for multi modal data

Authors: Shin Kamada; Takumi Ichimura

Addresses: Department of Intelligent Systems, Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asa-Minami-ku, Hiroshima, 731-3194, Japan ' Faculty of Management and Information Systems, Prefectural University of Hiroshima, 1-1-71, Ujina-Higashi, Minami-ku, Hiroshima 734-8558, Japan

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.

Keywords: multi modal data; automatically data arrangement method; deep learning; adaptive structural learning method; restricted boltzmann machine; deep belief network; shorting learning time.

DOI: 10.1504/IJCISTUDIES.2018.096183

International Journal of Computational Intelligence Studies, 2018 Vol.7 No.3/4, pp.169 - 191

Received: 19 Jan 2018
Accepted: 29 Mar 2018
Published online: 15 Nov 2018 *

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