  Filter pruning method introduces structural sparsity by removing selected
filters and is thus particularly effective for reducing complexity. Previous
works empirically prune networks from the point of view that filter with
smaller norm contributes less to the final results. However, such criteria has
been proven sensitive to the distribution of filters, and the accuracy may hard
to recover since the capacity gap is fixed once pruned. In this paper, we
propose a novel filter pruning method called Asymptotic Soft Cluster Pruning
(ASCP), to identify the redundancy of network based on the similarity of
filters. Each filter from over-parameterized network is first distinguished by
clustering, and then reconstructed to manually introduce redundancy into it.
Several guidelines of clustering are proposed to better preserve feature
extraction ability. After reconstruction, filters are allowed to be updated to
eliminate the effect caused by mistakenly selected. Besides, various decaying
strategies of the pruning rate are adopted to stabilize the pruning process and
improve the final performance as well. By gradually generating more identical
filters within each cluster, ASCP can remove them through channel addition
operation with almost no accuracy drop. Extensive experiments on CIFAR-10 and
ImageNet datasets show that our method can achieve competitive results compared
with many state-of-the-art algorithms.
