Convolutional Deep Feedforward Network for Image Classification

Abstract

Deep feedforward network (DFN) is a conceptual stepping stone of many well-known deep neural networks (DNN) in image classification and natural language application. The development on the standard DFN can rarely be found in the literature recently due to the popularity in convolutional networks. The recent trend of research focuses on the increment of the convolutional layers in a deeper and wider network architecture for achieving higher accuracy and lower misclassification rate. However, stacking the convolutional layers may not result in better accuracy due to the sparsity of interconnected of hidden nodes. In this paper, a convolutional deep feedforward network (C-DFN) is proposed to anlayse the performance of deep neural networks by increasing the number of fully-connected layers. C-DFN contains a Gabor-convolutional layer as a trainable feature extractor and followed by the four fully-connected layers. Experiments are conducted to evaluate the performance of proposed network with three other structures, i.e. deep belief network, deep feedforward network and convolutional deep belief network. The experimental results showed that C-DFN obtained the lowest average misclassfication rate of 9.41% in the image classification.