MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks

TL;DR

This paper introduces a multi-task channel pruning method for semantic segmentation networks that considers both classification and segmentation tasks to effectively reduce computational cost while maintaining performance.

Contribution

It proposes a novel multi-task pruning approach that jointly evaluates filter importance for segmentation and classification, improving efficiency over existing methods.

Findings

Achieved about 2x FLOPs reduction on DeepLabv3 with only 1% mIoU drop.

Reduced mIoU by about 1.3% on Cityscapes dataset.

Outperformed state-of-the-art pruning methods in experiments.

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.