Abstract The capacities of mobile and embedded devices are quite inadequate for the requirement of the storage capacity and computational resources of convolutional neural network models. Therefore, a lightweight convolutional neural network architecture, network with slice feature map, named SFNet, is proposed. The concept of slice block is introduced. By performing the “slice” processing on the output feature map of the network, each feature map segment is respectively sent to a convolution kernel of different sizes for convolution operation, and then the obtained feature map is concatenated. A simple 1×1 convolution is utilized to fuse the channels of the feature map. The experiments show that compared with the state-of-the-art lightweight convolutional neural networks, SFNet has fewer parameters and floating-point operations, and higher classification accuracy with the same number of convolution kernels and input feature map channels. Compared with the standard convolution, in the case of a significant reduction in network complexity, the classification accuracy is same or higher.
Fund:Supported by National Natural Science Foundation of China(No.61672467,61572443,11871438)
Corresponding Authors:
ZHENG Zhonglong. Ph.D., professor. His research inte-rests include pattern recognition, machine learning and image processing.
About author:: ZHANG Yufeng, master student. His research interests include deep learning. (LIU Huawen, Ph.D., professor. His research interests include machine learning and data mining.) (XIANG Daohong, Ph.D., associate professor. Her research interests include statistical machine learning, robust statistics and deep learning.) (HE Xiaowei, master, professor. His research interests include machine learning, ima-ge and video processing.) (LI Zhifei, master, lecturer. His research interests include pattern recognition, deep learning and virtual reality.) (HE Yiran, master student. Her research interests include machine learning.)
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2019, Vol. 32 
