  To apply deep CNNs to mobile terminals and portable devices, many scholars
have recently worked on the compressing and accelerating deep convolutional
neural networks. Based on this, we propose a novel uniform channel pruning
(UCP) method to prune deep CNN, and the modified squeeze-and-excitation blocks
(MSEB) is used to measure the importance of the channels in the convolutional
layers. The unimportant channels, including convolutional kernels related to
them, are pruned directly, which greatly reduces the storage cost and the
number of calculations. There are two types of residual blocks in ResNet. For
ResNet with bottlenecks, we use the pruning method with traditional CNN to trim
the 3x3 convolutional layer in the middle of the blocks. For ResNet with basic
residual blocks, we propose an approach to consistently prune all residual
blocks in the same stage to ensure that the compact network structure is
dimensionally correct. Considering that the network loses considerable
information after pruning and that the larger the pruning amplitude is, the
more information that will be lost, we do not choose fine-tuning but retrain
from scratch to restore the accuracy of the network after pruning. Finally, we
verified our method on CIFAR-10, CIFAR-100 and ILSVRC-2012 for image
classification. The results indicate that the performance of the compact
network after retraining from scratch, when the pruning rate is small, is
better than the original network. Even when the pruning amplitude is large, the
accuracy can be maintained or decreased slightly. On the CIFAR-100, when
reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy
of VGG-19 even improved by 0.54% after retraining.
