  Nowadays, it is still difficult to adapt Convolutional Neural Network (CNN)
based models for deployment on embedded devices. The heavy computation and
large memory footprint of CNN models become the main burden in real
application. In this paper, we propose a "Sparse Shrink" algorithm to prune an
existing CNN model. By analyzing the importance of each channel via sparse
reconstruction, the algorithm is able to prune redundant feature maps
accordingly. The resulting pruned model thus directly saves computational
resource. We have evaluated our algorithm on CIFAR-100. As shown in our
experiments, we can reduce 56.77% parameters and 73.84% multiplication in total
with only minor decrease in accuracy. These results have demonstrated the
effectiveness of our "Sparse Shrink" algorithm.
