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.
张雨丰,郑忠龙,刘华文,向道红,何小卫,李知菲,何依然,KHODJA abd Erraouf. 基于特征图切分的轻量级卷积神经网络[J]. 模式识别与人工智能, 2019, 32(3): 237-246.
ZHANG Yufeng, ZHENG Zhonglong, LIU Huawen, XIANG Daohong, HE Xiaowei, LI Zhifei, HE Yiran, KHODJA Abd Erraouf. A Lightweight Convolutional Neural Network Architecture with Slice Feature Map. , 2019, 32(3): 237-246.
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2019, Vol. 32 
