  Convolutional neural networks (CNNs) based solutions have achieved
state-of-the-art performances for many computer vision tasks, including
classification and super-resolution of images. Usually the success of these
methods comes with a cost of millions of parameters due to stacking deep
convolutional layers. Moreover, quite a large number of filters are also used
for a single convolutional layer, which exaggerates the parameter burden of
current methods. Thus, in this paper, we try to reduce the number of parameters
of CNNs by learning a basis of the filters in convolutional layers. For the
forward pass, the learned basis is used to approximate the original filters and
then used as parameters for the convolutional layers. We validate our proposed
solution for multiple CNN architectures on image classification and image
super-resolution benchmarks and compare favorably to the existing
state-of-the-art in terms of reduction of parameters and preservation of
accuracy.
