A Dual Rank-Constrained Filter Pruning Approach for Convolutional Neural Networks

Filter pruning has attracted increasing attentions to compress and accelerate the convolutional neural networks (CNNs) on computationally restricted devices. Existing related methods mainly focus on independently leveraging the spatial information of individual filters while ignoring the inner correlation among filters. In this letter, we propose a dual rank-constrained filter pruning approach for convolutional neural networks, in which the representation, clustering, and identification of representative filters are integrated into an adaptive graph regularization framework. Particularly, the proposed approach utilizes the low-rank constraint to capture the low-dimensional intrinsic representations of filters for the adaptive affinity graph construction and clustering. It is noteworthy that the original filters are projected as points on Grassmann manifold for geometrical structure preserving. Meanwhile, the high-rank constraint is employed to select the most informative filter for representing the cluster. Experimental results on CIFAR-10 dataset show that the proposed approach achieves competitive results compared with the state-of-the-art methods by 87.1% in VGGNet-16, 52.4% in GoogLeNet and 72.9% in ResNet-56 in terms of model parameters compression.