Papers › CHIP: CHannel Independence-based Pruning for Compact Neural Networks

CHIP: CHannel Independence-based Pruning for Compact Neural Networks

26 Oct 2021NeurIPS 2021 12arXiv:2110.13981archive 2025-07-28

Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Zonouz, Bo Yuan

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 https://github.com/Eclipsess/CHIP_NeurIPS2021.

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