Pruning Filters for Efficient ConvNets

Convolutional Neural Networks (CNNs) are extensively used in image and video recognition, natural language processing and other machine learning applications. The success of CNNs in these areas corresponds with a significant increase in the number of parameters and computation costs. Recent approaches towards reducing these overheads involve pruning and compressing the weights of various layers without hurting the overall CNN performance. However, using model compression to generate sparse CNNs mostly reduces parameters from the fully connected layers and may not significantly reduce the final computation costs. In this paper, we present a compression technique for CNNs, where we prune the filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole planes in the network, together with their connecting convolution kernels, the computational costs are reduced significantly. In contrast to other techniques proposed for pruning networks, this approach does not result in sparse connectivity patterns. Hence, our techniques do not need the support of sparse convolution libraries and can work with the most efficient BLAS operations for matrix multiplications. In our results, we show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by upto 38% while regaining close to the original accuracy by retraining the networks.