  Pruning has become a promising technique used to compress and accelerate
neural networks. Existing methods are mainly evaluated on spare labeling
applications. However, dense labeling applications are those closer to real
world problems that require real-time processing on resource-constrained mobile
devices. Pruning for dense labeling applications is still a largely unexplored
field. The prevailing filter channel pruning method removes the entire filter
channel. Accordingly, the interaction between each kernel in one filter channel
is ignored.
  In this study, we proposed kernel cluster pruning (KCP) to prune dense
labeling networks. We developed a clustering technique to identify the least
representational kernels in each layer. By iteratively removing those kernels,
the parameter that can better represent the entire network is preserved; thus,
we achieve better accuracy with a decent model size and computation reduction.
When evaluated on stereo matching and semantic segmentation neural networks,
our method can reduce more than 70% of FLOPs with less than 1% of accuracy
drop. Moreover, for ResNet-50 on ILSVRC-2012, our KCP can reduce more than 50%
of FLOPs reduction with 0.13% Top-1 accuracy gain. Therefore, KCP achieves
state-of-the-art pruning results.
