  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 on representative
image classification tasks demonstrate the superiority of our White-Box over
many state-of-the-arts. For instance, on CIFAR-10, it reduces 65.23% FLOPs with
even 0.62% accuracy improvement for ResNet-110. On ILSVRC-2012, White-Box
achieves a 45.6% FLOPs reduction with only a small loss of 0.83% in the top-1
accuracy for ResNet-50.
