Analyzing Filters Toward Efficient ConvNet

Deep convolutional neural network (ConvNet) is a promising approach for high-performance image classification. The behavior of ConvNet is analyzed mainly based on the neuron activations, such as by visualizing them. In this paper, in contrast to the activations, we focus on filters which are main components of ConvNets. Through analyzing two types of filters at convolution and fully-connected (FC) layers, respectively, on various pre-trained ConvNets, we present the methods to efficiently reformulate the filters, contributing to improving both memory size and classification performance of the ConvNets. They render the filter bases formulated in a parameter-free form as well as the efficient representation for the FC layer. The experimental results on image classification show that the methods are favorably applied to improve various ConvNets, including ResNet, trained on ImageNet with exhibiting high transferability on the other datasets.