  Filter pruning has drawn more attention since resource constrained platform
requires more compact model for deployment. However, current pruning methods
suffer either from the inferior performance of one-shot methods, or the
expensive time cost of iterative training methods. In this paper, we propose a
balanced filter pruning method for both performance and pruning speed. Based on
the filter importance criteria, our method is able to prune a layer with
approximate layer-wise optimal pruning rate at preset loss variation. The
network is pruned in the layer-wise way without the time consuming
prune-retrain iteration. If a pre-defined pruning rate for the entire network
is given, we also introduce a method to find the corresponding loss variation
threshold with fast converging speed. Moreover, we propose the layer group
pruning and channel selection mechanism for channel alignment in network with
short connections. The proposed pruning method is widely applicable to common
architectures and does not involve any additional training except the final
fine-tuning. Comprehensive experiments show that our method outperforms many
state-of-the-art approaches.
