Structured network pruning excels non-structured methods because they can
take advantage of the thriving developed parallel computing techniques. In this
paper, we propose a new structured pruning method. Firstly, to create more
structured redundancy, we present a data-driven loss function term calculated
from the correlation coefficient matrix of different feature maps in the same
layer, named CCM-loss. This loss term can encourage the neural network to learn
stronger linear representation relations between feature maps during the
training from the scratch so that more homogenous parts can be removed later in
pruning. CCM-loss provides us with another universal transcendental
mathematical tool besides L*-norm regularization, which concentrates on
generating zeros, to generate more redundancy but for the different genres.
Furthermore, we design a matching channel selection strategy based on principal
components analysis to exploit the maximum potential ability of CCM-loss. In
our new strategy, we mainly focus on the consistency and integrality of the
information flow in the network. Instead of empirically hard-code the retain
ratio for each layer, our channel selection strategy can dynamically adjust
each layer's retain ratio according to the specific circumstance of a
per-trained model to push the prune ratio to the limit. Notably, on the
Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only
1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs
are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet
dataset, our approach achieves 42.8% and 47.3% storage and computation
reductions, respectively, with an accuracy of 76.23%. Our code is available at
this https URL

提出了一种新的结构修剪方法，在训练期间利用 CCM-loss 来促使神经网络学习强大的线性表示关系，然后设计了一种基于主成分分析的匹配通道选择策略来最大限度地利用 CCM-loss 的潜在能力，达到更高的剪枝率和减少计算量。