Filter pruning has been widely used for neural network compression because of
its enabled practical acceleration. To date, most of the existing filter
pruning works explore the importance of filters via using intra-channel
information. In this paper, starting from an inter-channel perspective, we
propose to perform efficient filter pruning using Channel Independence, a
metric that measures the correlations among different feature maps. The less
independent feature map is interpreted as containing less useful
information$/$knowledge, and hence its corresponding filter can be pruned
without affecting model capacity. We systematically investigate the
quantification metric, measuring scheme and sensitiveness$/$reliability of
channel independence in the context of filter pruning. Our evaluation results
for different models on various datasets show the superior performance of our
approach. Notably, on CIFAR-10 dataset our solution can bring $0.90\%$ and
$0.94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models,
respectively, and meanwhile the model size and FLOPs are reduced by $42.8\%$
and $47.4\%$ (for ResNet-56) and $48.3\%$ and $52.1\%$ (for ResNet-110),
respectively. On ImageNet dataset, our approach can achieve $40.8\%$ and
$44.8\%$ storage and computation reductions, respectively, with $0.15\%$
accuracy increase over the baseline ResNet-50 model. The code is available at
this https URL

本文从跨通道的角度出发，提出了一种基于通道独立度的高效的滤波器剪枝方法，该方法通过减少含有较少有用信息的特征图的数量，从而减小了模型容量，提高了模型的计算和存储效率。实验结果表明，在不影响精度的情况下，该方法可以显著地减小模型大小和 FLOPs 并提高分类精度。