一种耦合深度信念网络的图像识别方法

针对网络层数增加带来的梯度消失问题，提出了一种耦合深度信念网络的图像识别方法．该方法将“跨层”连接引入到深度信念网络中并应用于图像识别．给出了耦合深度信念网络的结构示意图及其参数更新方法，并在相同数据集和网络层数情况下比较了具有最佳参数的深度信念网络与最佳参数的耦合深度信念网络的识别性能，分析了“跨层”连接中主、次线耦合比例对网络性能的影响，且与几种经典的深度学习方法进行了对比．实验结果显示，耦合深度信念网络在收敛速度与识别精度上均优于深度信念网络．同时，相比于经典的深度网络，文中所提方法获得了良好的识别性能．这说明采用“跨层”耦合方式可有效缓解深度信念网络训练过程中出现的梯度消失问题，提高网络的识别性能．

Coupled-deep belief network based method for image recognition

Aiming at the gradient vanishing problem caused by the increasing number of network layers, an image recognition method based on the Coupled-Deep Belief Network (C-DBN) is proposed, which introduces the cross-layer linkage to the Deep Belief Network (DBN). The structure and the parameter updating method for the C-DBN are given in detail, while the performance of the DBN and that of the C-DBN are compared with respect to their respective best parameters and the same net-depth on two image datasets. Moreover, the impact of the weights used in the coupling between the primary line and the secondary line is analyzed at the cross-layer linkage. Besides, several classic deep learning based methods are compared with the proposed C-DBN. Experimental results show that the C-DBN is superior to the DBN in terms of convergence speed and accuracy. And, a good performance is achieved by the proposed method in comparison with some classical deep learning methods. This means that the usage of cross-layer linkage can alleviate the gradient vanishing problems effectively in the DBN training, which helps to improve the following recognition performance.