A Compact Depth Separable Convolutional Image Filter for Clinical Color Perception Test

Abstract

Deep convolutional neural networks have achieved good performance in the application of computer vision, but there are also problems, such as a large amount of computation, time consuming, and high memory demand. In this paper, a depthwise separable convolution filter pruning method based on PCA is proposed. First, this paper uses depthwise separable convolution to replace the conventional convolution in ResNet to reduce the number of parameters and the amount of computation in the network. The specific operation process is to first use depthwise convolution to separate the spatial dimension to increase the network width and expand the range of feature extraction, and then use pointwise convolution to reduce the computational complexity of conventional convolution operation. Second, PCA is used to distinguish stacked similar filters and perform dimensionality reduction, which not only alleviates the dimensional disaster, but also achieves compression of data and minimizes information loss. Experimental results show that this method can significantly improve the calculation speed and accuracy of the deep convolutional neural network model, and further compress the model size. On the clinical Color Perception Test Chart, this method reduced the amount of model parameters and MACs on ResNet by about 91% while maintaining the test accuracy at about 95%. With almost no loss of accuracy, this method greatly shortened the running time of the model.