  Channel pruning has demonstrated its effectiveness in compressing ConvNets.
In many related arts, the importance of an output feature map is only
determined by its associated filter. However, these methods ignore a small part
of weights in the next layer which disappears as the feature map is removed.
They ignore the phenomenon of weight dependency. Besides, many pruning methods
use only one criterion for evaluation and find a sweet spot of pruning
structure and accuracy in a trial-and-error fashion, which can be
time-consuming. In this paper, we proposed a channel pruning algorithm via
multi-criteria based on weight dependency, CPMC, which can compress a
pre-trained model directly. CPMC defines channel importance in three aspects,
including its associated weight value, computational cost, and parameter
quantity. According to the phenomenon of weight dependency, CPMC gets channel
importance by assessing its associated filter and the corresponding partial
weights in the next layer. Then CPMC uses global normalization to achieve
cross-layer comparison. Finally, CPMC removes less important channels by global
ranking. CPMC can compress various CNN models, including VGGNet, ResNet, and
DenseNet on various image classification datasets. Extensive experiments have
shown CPMC outperforms the others significantly.
