2018/11/17 by Van-Thanh Hoang, Hoang, Van-Thanh, Kang-Hyun Jo +1 · 4 citations
Computer Science · Mathematics · #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolution (computer science) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Geometry #IoT and Edge/Fog Computing #Mathematical analysis #Mathematics #Network Security and Intrusion Detection #Pyramid (geometry) #Separable space #cs.CV
paper · pdf · doi:10.48550/arxiv.1811.07083
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/11/17 · openalex publication_date 2018/11/17 · arxiv updated 2018/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 and embedded vision applications. Many works try to build networks as small as possible while still have acceptable performance. The state-of-the-art architecture is MobileNets. They use Depthwise Separable Convolution (DWConvolution) in place of standard Convolution to reduce the size of networks. This paper describes an improved version of MobileNet, called Pyramid Mobile Network. Instead of using just a 3× 3 kernel size for DWConvolution like in MobileNet, the proposed network uses a pyramid kernel size to capture more spatial information. The proposed architecture is evaluated on two highly competitive object recognition benchmark datasets (CIFAR-10, CIFAR-100). The experiments demonstrate that the proposed network achieves better performance compared with MobileNet as well as other state-of-the-art networks. Additionally, it is more flexible in fine-tuning the trade-off between accuracy, latency and model size than MobileNets.