  This paper focuses on channel pruning for semantic segmentation networks.
Previous methods to compress and accelerate deep neural networks in the
classification task cannot be straightforwardly applied to the semantic
segmentation network that involves an implicit multi-task learning problem via
pre-training. To identify the redundancy in segmentation networks, we present a
multi-task channel pruning approach. The importance of each convolution filter
\wrt the channel of an arbitrary layer will be simultaneously determined by the
classification and segmentation tasks. In addition, we develop an alternative
scheme for optimizing importance scores of filters in the entire network.
Experimental results on several benchmarks illustrate the superiority of the
proposed algorithm over the state-of-the-art pruning methods. Notably, we can
obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$
mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on
Cityscapes dataset, respectively.
