More-Similar-Less-Important: Filter Pruning VIA Kmeans Clustering
Zili Liu, Peisong Wang, Zaixing Li · 2021
Recent works indicate that traditional norm-based filter pruning methods are sensitive to the distribution of filters across different layers. Therefore, an additional sensitivity analysis is necessary to determine the pruning ratio of each layer, which is time-consuming. To solve this problem, many works proposed complicated pruning algorithms to search for the optimal pruned structures automatically under given FLOPs, which also training and time-consuming. In this paper, we propose a novel predefined filter pruning method for convolutional neural networks, called Filter Pruning via KMeans Clustering (FPKM), to identify the redundant filters based on the similarity of filters, regardless of the distribution of filters. FPKM eliminates the analysis of sensitivity to pruning and the searching process for pruned structure while ensuring accuracy. Extensive experiments on CIFAR10 and ImageNet datasets show that our FPKM method achieves significant improvement compared with state-of-the-art filter pruning methods.