# MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks Source: [MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:30:35.914Z Authors: Xinghao Chen, Yiman Zhang, Yunhe Wang Published: 2020-07-16T15:03:01.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()