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Carrying out CNN Channel Pruning in a White Box by Zhang, Yuxin; Lin, Mingbao; Lin, Chia-Wen; Chen, Jie; Huang, Feiyue; Wu, Yongjian; Tian, Yonghong; Ji, Rongrong is a scholarly article available to read on EtoBox.

What is Carrying out CNN Channel Pruning in a White Box about?

Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which tends to reduce the final classification errors while failing to consider the internal influence of each channel. In this paper, we conduct channel pruning in a white box. Through deep visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we choose to preserve channels contributing to most categories. Specifically, to model the contribution of each channel to differentiating categories, we develop a class-wise mask for each channel, implemented in a dynamic training manner w.r.t. the input image's category. On the basis of the learned class-wise mask, we perform a global voting mechanism to remove channels with less category discrimination. Lastly, a fine-tuning process is conducted to recover the performance of the pruned model. To our best knowledge, it is the first time that CNN interpretability theory is considered to guide channel pruning. Extensive experiments o

Author

Zhang, Yuxin; Lin, Mingbao; Lin, Chia-Wen; Chen, Jie; Huang, Feiyue; Wu, Yongjian; Tian, Yonghong; Ji, Rongrong

Published

2021

Language

EN