Neural Network Light Weighting Approach Using Multi-Metric Evaluation of Convolution Kernels

The prevalence of convolutional neural networks is hindered by their intricate structure and voluminous parameters, which consume significant processing resources during both training and inference. This study proposes a novel approach that involves treating convolutional kernels as tensors, utilizing suitable priori metrics to gauge their effectiveness, and employing a clustering algorithm to eliminate redundant convolutional kernels for network pruning. To measure the convolutional kernels, we use appropriate priori metrics and density-Based spatial clustering of applications with noise (DBSCAN) to cluster the convolutional kernels so that the cluster centroids are the reserved convolution kernels. The experimental results show that the method can effectively perform network lightweight while maintaining high accuracy. At the same time, the method can improve the efficiency of convolutional kernel utilization, thus reducing the computational resource consumption of the model. Empirical analyses conducted on datasets indicate that, in certain instances, the proposed pruning method outperforms established state-of-the-art methods.