  Since the convolutional neural networks are often trained with redundant
parameters, it is possible to reduce redundant kernels or filters to obtain a
compact network without dropping the classification accuracy. In this paper, we
propose a filter pruning method using the hierarchical group sparse
regularization. It is shown in our previous work that the hierarchical group
sparse regularization is effective in obtaining sparse networks in which
filters connected to unnecessary channels are automatically close to zero.
After training the convolutional neural network with the hierarchical group
sparse regularization, the unnecessary filters are selected based on the
increase of the classification loss of the randomly selected training samples
to obtain a compact network. It is shown that the proposed method can reduce
more than 50% parameters of ResNet for CIFAR-10 with only 0.3% decrease in the
accuracy of test samples. Also, 34% parameters of ResNet are reduced for
TinyImageNet-200 with higher accuracy than the baseline network.
