Deep Residual Networks with Pyramid Depthwise Separable Convolution

Van-Thanh Hoang, Ajmal Shahbaz, Kang-Hyun Jo

Abstract: Convolutional neural networks (CNNs) have shown remarkable performance in various computer vision tasks in recent years. However, the increasing model size has raised challenges in adopting them in real-time applications as well as mobile or embedded vision applications. Many works try to build networks as small as possible while still have acceptable performance by using Depthwise Separable Convolution (DWConvolution) in place of standard Convolution. This paper proposes a network which uses an extended version of DWCon-volution, called Pyramid DW Convolution. Instead of using just a 3 × 3 kernel size for DWConvolution, the proposed network uses a pyramid kernel size to capture more spatial information. The proposed architecture is evaluated on the highly competitive object recognition benchmark datasets ImageNet and the two CIFAR (CIFAR-10, CIFAR-100). The experiments demonstrate that the proposed network achieves better performance compared with other state-of-the-art networks.