MSFC: Deep Feature Compression in Multi-Task Network
Zhicong Zhang, Mengyang Wang, Mengyao Ma, Jiahui Li, Xiaopeng Fan
Abstract
With the remarkable success of deep learning, a novel AI-deployment strategy on mobile devices called collaborative intelligence (CI) is proposed recently, which can greatly improve the efficiency of neural network by distributing work-loads between mobile devices and the cloud. In order to reduce transmission overhead, feature maps obtained from mobile devices need to be compressed before being transmitted to the cloud. In this paper, we propose a multi-scale feature compression (MSFC) framework for applying complex multi-task learning network in CI deployment scenarios, which consists of a multi-scale feature fusion (MSFF) module, a single-stream feature codec (SSFC) and a multi-scale feature reconstruction (MSFR) module. When applied to the popular multi-task network Mask R-CNN, experimental results show that with less than 2% accuracy degradation, the proposed MSFC can compress the 32-bit floating point feature to 0.012 bits on average.