NETWORK PRUNING USING LINEAR DEPENDENCY ANALYSIS ON FEATURE MAPS

Network pruning can be achieved by removing redundant channels. In this paper, we regard a channel 'redundant' if its output is linearly dependent with respect to those of other channels. Inspired by this, we propose an efficient pruning method, named as LDFM, by linear dependency analysis on all the feature maps of each individual layer. Specifically, for each layer, by applying the QR decomposition with column pivoting (PQR) on the matrix consisting of all feature maps, those channels corresponding to small absolute diagonal elements of the R matrix from the PQR decomposition are identified as redundant, and are pruned naturally. Although pruning these channels causes loss of information and hence degrades accuracy, the accuracy of the pruned network can be easily recovered by fine-tuning, as the lost information in the pruned channels can be recovered from that in the retained channels. Extensive experiments demonstrate that LDFM makes great improvement on accuracy with similar parameters and FLOPs as other methods, and achieves the state-of-the-art results on several different benchmarks and networks.

Automated summary

The paper introduces a method for network pruning based on linear dependency analysis, which improves network accuracy while maintaining similar parameters and computational complexity. Experimental results demonstrate that this method achieves state-of-the-art results on several different benchmarks and networks.