关键词: 卷积神经网络, 图像分类, 网络模型压缩, 知识提取, 实时性

Abstract: For the problem of the convolution neural network’s high classification accuracy but poor real-time performance in the image classification task, a distilling knowledge algorithm with scale factor is proposed. This method adds the scale factor to measure the close relationship between the sample classes based on the existing “distilling knowledge” algorithm, enrich the network compression means, which can make the convolution neural network perform “distilling knowledge” more accurately. The principle is to use the scale factor error as part of the cost function to adjust the neural parameters of the neural network, which makes the generalization ability of neural network more similar to the compression reference network with better classification performance ability. The results show that the neural network compression algorithm with scale factor can describe the inter-class close relation of the training set more detailly and have better training performance than the original “distilling knowledge” algorithm and then the neural network with better generalization performance and higher precision is trained. It is realized that a large degree of reduction in neural network classification time under the premise that network classification accuracy is reduced as little as possible in order to achieve the purpose of network compression.

Key words: convolution neural network, image classification, network model compression, distilling the knowledge, real-time