  Convolutional neural networks (CNNs) have demonstrated their capability to
solve different kind of problems in a very huge number of applications.
However, CNNs are limited for their computational and storage requirements.
These limitations make difficult to implement these kind of neural networks on
embedded devices such as mobile phones, smart cameras or advanced driving
assistance systems. In this paper, we present a novel layer named Hybrid Cosine
Based Convolution that replaces standard convolutional layers using cosine
basis to generate filter weights. The proposed layers provide several
advantages: faster convergence in training, the receptive field can be
increased at no cost and substantially reduce the number of parameters. We
evaluate our proposed layers on three competitive classification tasks where
our proposed layers can achieve similar (and in some cases better) performances
than VGG and ResNet architectures.
