论文标题
采取更有效和有效的推论：多参与者的共同决策
Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants
论文作者
论文摘要
现有的方法通过优化本地架构或加深网络来改善卷积神经网络的性能，往往会大大增加模型的大小。为了部署并将神经网络应用于需求巨大的边缘设备，减少网络的规模至关重要。但是，很容易通过压缩网络来降低图像处理的性能。在本文中，我们提出了一种适合边缘设备的方法，同时提高了推理的效率和有效性。多参与者（主要包含多层和多网络）的联合决策可以达到更高的分类精度（CIFAR-10的0.26％，最多最多为CIFAR-100），具有相似的经典卷积神经网络参数的总数。
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.
