  In convolutional neural networks (CNNs), the filter grouping in convolution
layers is known to be useful to reduce the network parameter size. In this
paper, we propose a new logarithmic filter grouping which can capture the
nonlinearity of filter distribution in CNNs. The proposed logarithmic filter
grouping is installed in shallow CNNs applicable in a mobile application.
Experiments were performed with the shallow CNNs for classification tasks. Our
classification results on Multi-PIE dataset for facial expression recognition
and CIFAR-10 dataset for object classification reveal that the compact CNN with
the proposed logarithmic filter grouping scheme outperforms the same network
with the uniform filter grouping in terms of accuracy and parameter efficiency.
Our results indicate that the efficiency of shallow CNNs can be improved by the
proposed logarithmic filter grouping.
