  With the increase of structure complexity, convolutional neural networks
(CNNs) take a fair amount of computation cost. Meanwhile, existing research
reveals the salient parameter redundancy in CNNs. The current pruning methods
can compress CNNs with little performance drop, but when the pruning ratio
increases, the accuracy loss is more serious. Moreover, some iterative pruning
methods are difficult to accurately identify and delete unimportant parameters
due to the accuracy drop during pruning. We propose a novel adversarial
iterative pruning method (AIP) for CNNs based on knowledge transfer. The
original network is regarded as the teacher while the compressed network is the
student. We apply attention maps and output features to transfer information
from the teacher to the student. Then, a shallow fully-connected network is
designed as the discriminator to allow the output of two networks to play an
adversarial game, thereby it can quickly recover the pruned accuracy among
pruning intervals. Finally, an iterative pruning scheme based on the importance
of channels is proposed. We conduct extensive experiments on the image
classification tasks CIFAR-10, CIFAR-100, and ILSVRC-2012 to verify our pruning
method can achieve efficient compression for CNNs even without accuracy loss.
On the ILSVRC-2012, when removing 36.78% parameters and 45.55% floating-point
operations (FLOPs) of ResNet-18, the Top-1 accuracy drop are only 0.66%. Our
method is superior to some state-of-the-art pruning schemes in terms of
compressing rate and accuracy. Moreover, we further demonstrate that AIP has
good generalization on the object detection task PASCAL VOC.
