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.

中文翻译：

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迈向更高效、更有效的推理：多方共同决策

通过优化局部架构或深化网络来提高卷积神经网络性能的现有方法往往会显着增加模型的大小。为了将神经网络部署和应用到需求量很大的边缘设备，减少网络规模是非常关键的。然而，通过压缩网络很容易降低图像处理的性能。在本文中，我们提出了一种适用于边缘设备的方法，同时提高了推理的效率和有效性。多参与者的联合决策，主要包含多层和多网络，可以达到更高的分类精度（CIFAR-10 上为 0.26%，CIFAR-100 上最多为 4.49%）与经典卷积神经网络的参数总数相似网络。