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This paper focuses on channel pruning for semantic segmen-tation networks. Previous methods to compress and acceler-ate deep neural networks in the classification task cannot be straightforwardly applied to the semantic segmentation net-work 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 w.r.t. the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we de-velop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on sev-eral benchmarks illustrate the superiority of the proposed al-gorithm over the state-of-the-art pruning methods. Notably, we can obtain an about 2 x FLOPs reduction on DeepLabv3 with only an about 1 &#x0025; mIoU drop on the PASCAL VOC 2012 dataset and an about 1.3&#x0025; mIoU drop on Cityscapes dataset, respectively.

MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks