Along with the recent development of Convolutional Neural Network (CNN) and its multilayering, it is important to reduce the amount of computation and the amount of data associated with convolution processing. Some compression methods of convolutional filters using low-rank approximation have been studied. The common goal of these studies is to accelerate the computation wherever possible while maintaining the accuracy of image recognition. In this paper, we investigate the trade-off between the compression error by low-rank approximation and the computational complexity for the state-of-the-arts CNN model.