  This paper aims to simultaneously accelerate and compress off-the-shelf CNN
models via filter pruning strategy. The importance of each filter is evaluated
by the proposed entropy-based method first. Then several unimportant filters
are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to
recover its generalization ability which is damaged during filter pruning. Our
method can reduce the size of intermediate activations, which would dominate
most memory footprint during model training stage but is less concerned in
previous compression methods. Experiments on the ILSVRC-12 benchmark
demonstrate the effectiveness of our method. Compared with previous filter
importance evaluation criteria, our entropy-based method obtains better
performance. We achieve 3.3x speed-up and 16.64x compression on VGG-16, 1.54x
acceleration and 1.47x compression on ResNet-50, both with about 1% top-5
accuracy decrease.
