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A KM-Net Model Based on k-Means Weight Initialization for Images Classification

Convolutional neural networks (CNNs) have been successfully applied in the fields of image classification. The training cost of various large CNN models increases rapidly, which has become a challenging task to balance the recognition accuracy and the convergence speed in the training procedure of CNN models for a particular dataset. In this paper, a KM-Net model with convolutional kernels in the first layer initialized by k-Means clustering algorithm is proposed. Experimental results show that when compared with some other methods on the most widely-used SVHN dataset and ASL dataset, the proposed KM-Net model can not only improve the recognition accuracy, but also accelerate the convergence speed of the training procedure.