Building Correlations between Filters in Convolutional Neural Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Article number7782341
Pages (from-to)3218-3229
Journal / PublicationIEEE Transactions on Cybernetics
Volume47
Issue number10
Early online date13 Dec 2016
StatePublished - Oct 2017

Abstract

In this paper, a new optimization approach is designed for convolutional neural network (CNN) which introduces explicit logical relations between filters in the convolutional layer. In a conventional CNN, the filters’ weights in convolutional
layers are separately trained by their own residual errors, and the relations of these filters are not explored for learning. Different from the traditional learning mechanism, the proposed correlative filters (CFs) are initiated and trained jointly in accordance with predefined correlations, which are efficient to work cooperatively and finally make a more generalized optical system. The improvement in CNN performance with the proposed CF is verified on five benchmark image classification datasets, including CIFAR-10, CIFAR-100, MNIST, STL-10, and street view house number. The comparative experimental results demonstrate that the proposed approach outperforms a number of state-of-the-art CNN approaches.

Research Area(s)

  • Convolutional kernel, convolutional neural network (CNN), correlative filters (CFS), filter modeling, image classification

Citation Format(s)

Building Correlations between Filters in Convolutional Neural Networks. / Wang, Hanli; Chen, Peiqiu; Kwong, Sam.

In: IEEE Transactions on Cybernetics, Vol. 47, No. 10, 7782341, 10.2017, p. 3218-3229.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal