RSNet: A Compact Relative Squeezing Net for Image Recognition

Abstract

Convolutional neural networks(CNN) are showing powerful performance on image recognition tasks. However, when CNN is applied to mobile devices, with limited computing and memory resource, it requires more compact design to maintain a relatively high performance. In this paper, we propose Relative Squeezing Net(RSNet) that provides technical insight into CNN structure for designing a compact model. In an endeavor to improve CondenseNet, we introduce Relative-Squeezing bottleneck where output is weighted percentage of input channels. The design of our bottleneck can transmit diverse and most useful features at all stages. We also employ multiple compression layers to constrain the output channels of feature maps which can eliminate superfluous feature maps and transmit powerful representations to next layers. We evaluate our model on two benchmark datasets; CIFAR and ImageNet. Experimental results show that RSNet achieves state-of-the-art results with less parameters and FLOPs and is more efficient than compact architectures such as CondenseNet, MobileNet and ShuffleNet.