  Artificial neural networks (ANNs) especially deep convolutional networks are
very popular these days and have been proved to successfully offer quite
reliable solutions to many vision problems. However, the use of deep neural
networks is widely impeded by their intensive computational and memory cost. In
this paper, we propose a novel efficient network pruning method that is
suitable for both non-structured and structured channel-level pruning. Our
proposed method tightens a sparsity constraint by gradually removing network
parameters or filter channels based on a criterion and a schedule. The
attractive fact that the network size keeps dropping throughout the iterations
makes it suitable for the pruning of any untrained or pre-trained network.
Because our method uses a $L_0$ constraint instead of the $L_1$ penalty, it
does not introduce any bias in the training parameters or filter channels.
Furthermore, the $L_0$ constraint makes it easy to directly specify the desired
sparsity level during the network pruning process. Finally, experimental
validation on extensive synthetic and real vision datasets show that the
proposed method obtains better or competitive performance compared to other
states of art network pruning methods.
