Filter combination learning for CNN model compression

In this paper, we propose a new method for generating convolution filters of a convolutional neural network (CNN) model as linear combinations of only a few basis filters that are provided as input features. In our approach, best coefficients of the linear combinations are searched (trained) with the given input basis filters (IBFs) to reconstruct the convolution filter parameters. Since all the convolution filters can be generated by the linear combinations of the IBFs, the size of a CNN model can be compressed if the number of coefficients for the linear combinations is less than that of filter parameters. Our primary goal is to investigate the possibility of expressing filters with a small set of IBFs by linear combinations. The second goal is to compress a model so that it can be beneficial when the model is distributed and stored (particularly downloaded to mobile devices through Wi-Fi).