A graphical approach for filter pruning by exploring the similarity relation between feature maps

Abstract

The vast majority of repetitive pruning and retraining techniques used on CNNs require multi-stage optimization, which undermines the potential computing savings from pruning. The similarity relationship between the output feature maps of the filters is first observed and represented as a graphical model in this paper in order to solve this issue. Vertex redundancy in a graph is determined using the degree of a vertex and the weight of its edges. Pruning is then carried out “one-shot” by removing the filters associated with redundant feature maps from each layer. In order to preserve performance, the network must finally be retrained. The CIFAR-10 dataset is identified using the classifier produced by applying this proposed methodology to several deep learning models. In this instance, both the parameters reduction and FLOPs reduction of the trimmed model are significantly improved. This method, tested on VGG-16, reduces parameters by 90.8% and FLOPs by 60.0% while sacrificing little accuracy (=0.16%) in comparison to the baseline. The results based on ResNet-110 show that applying the proposed approach can reduce FLOPs and parameters by 70.4% while preserving baseline accuracy with a loss of just 0.05%.