Compression techniques for deep neural networks are important for
implementing them on small embedded devices. In particular, channel-pruning is
a useful technique for realizing compact networks. However, many conventional
methods require manual setting of compression ratios in each layer. It is
difficult to analyze the relationships between all layers, especially for
deeper models. To address these issues, we propose a simple channel-pruning
technique based on attention statistics that enables to evaluate the importance
of channels. We improved the method by means of a criterion for automatic
channel selection, using a single compression ratio for the entire model in
place of per-layer model analysis. The proposed approach achieved superior
performance over conventional methods with respect to accuracy and the
computational costs for various models and datasets. We provide analysis
results for behavior of the proposed criterion on different datasets to
demonstrate its favorable properties for channel pruning.

本研究提出了一种基于注意统计的简单通道剪枝技术，使用单个压缩比替代每层模型的分析，实现了自动通道选择，取得了超越常规方法的性能提升，训练不同模型和数据集的结果表明该方法减少了计算成本且更加准确。