MSNet: Multi-Resolution Synergistic Networks for Adaptive Inference

Abstract

Adaptive inference with multiple networks has attracted much attention for resource-limited image classification. It assumes that a large portion of test samples can be correctly classified by small networks with fewer layers or channels, which poses a great challenge for them. In this paper, we argue that large networks have abilities to help the small ones address this challenge if fully explored. To this end, we propose a multi-resolution synergistic network (MSNet) using two different kinds of fusion modules. The first one is a cross-branch aggregation module, which aims to transfer the high-resolution features to the low-resolution ones between neighboring branches. The other one is an adaptive distillation module, whose purpose is feeding the discriminative ability of the large network to the other ones. Via these two modules, the small networks will be powerful enough to correctly classify large numbers of test samples, thus improving the classification accuracy and inference efficiency. We evaluate MSNet on three benchmark datasets: CIFAR-10, CIFAR-100, and ImageNet. Experimental results show that our network can obtain better results than several state-of-the-art networks in both anytime classification and budgeted batch classification settings. The code is available at https://github.com/bigdata-qian/MSNet-Pytorch.