OGCNet: Overlapped group convolution for deep convolutional neural networks

Abstract

The deployment of deep convolutional neural networks (CNNs) is heavily constrained by its high computational costs and parameter redundancy. For this reason, general group convolution (GGC) and depthwise convolution (DWC) were proposed, but they limited the information transfer in the channel dimension. In this paper, a novel and efficient overlapped group convolution (OGC) is proposed to improve the information transfer between channels. In OGC, the input feature maps can be overlapped between different groups. Compared with GGC, OGC has better information transfer in the channel dimension without additional parameters and computational cost. In theory, OGC unifies the standard convolution (SDC), GGC, and DWC. In other words, SDC, GGC, and DWC all belong to the special cases of OGC. In OGC, two flexible hyperparameters are defined, the number of input feature maps in each group (g) and the stride between adjacent groups (s), which make OGC more flexible and can make the trade-off between accuracy and parameters. The performance of OGC is analyzed in terms of parameters, computational cost, accuracy, run time, etc. The classification and object detection tasks are used to evaluate the performance of OGC. Experimental results show that the OGC has higher accuracy and is more efficient than the corresponding SDC, GGC, and DWC. The ratio of the two hyperparameters in OGC has a great impact on accuracy. When 23<sg<67, OGC has higher accuracy than others. The proposed OGC is more stable and robust than GGC.