  Existing approaches to improve the performances of convolutional neural
networks by optimizing the local architectures or deepening the networks tend
to increase the size of models significantly. In order to deploy and apply the
neural networks to edge devices which are in great demand, reducing the scale
of networks are quite crucial. However, It is easy to degrade the performance
of image processing by compressing the networks. In this paper, we propose a
method which is suitable for edge devices while improving the efficiency and
effectiveness of inference. The joint decision of multi-participants, mainly
contain multi-layers and multi-networks, can achieve higher classification
accuracy (0.26% on CIFAR-10 and 4.49% on CIFAR-100 at most) with similar total
number of parameters for classical convolutional neural networks.
