Abstract
Lightweight model is mainly applied to maintain performance and reduce the amount of parameters, simplifying the complex laboratory model to the mobile embedded device. We present a multi-scale dilated lightweight network model for image recognition. ShuffleNet is an classical lightweight neural network that proposes channel shuffle to help exchange information between groups during group convolution. However, ShuffleNet does not make full use of each group of information after channel shuffle. Since channel shuffle guarantees that each group contains the information of other groups, in this paper, we propose to process the grouping data with different dilated convolution, and obtain the multi-scale information of different receptive fields without increasing parameters. At the same time, we make an improvement on the network model to reduce the gridding artifacts caused by dilated convolution. Experiments on CIFAR-10 and EMNIST show that the improved algorithm performs better than traditional method.
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