基于衰减因子的双通道神经网络图像分类算法

为解决深度卷积神经网络(convolutional neural networks, CNN)难以训练的问题,提出一种快速、高效的双通道神经网络(dual channel neural networks, DCNN),该神经网络由直通通道和卷积通道两种通道构成,直通通道负责保障深度网络的畅通性,卷积通道负责深度网络的学习。考虑到深层网络在训练时容易出现性能不稳定的问题,在卷积通道上引入卷积衰减因子,对其响应数据进行约束。设计一种“双池化层”对同一特征图进行降采样,不仅可以防止训练过拟合,还能保证各通道的维度一致性。在3个图像数据集CIFAR-10、CIFAR-100和MNIST上的实验结果表明,无论是神经网络的可训练深度、稳定性和分类精度,DCNN都明显优于现有的深度卷积神经网络。

Image classification for dual-channel neural networks based on attenuation factor

To resolve the problem of training in deep convolutional neural networks (CNN), a fast and efficient dual-channel neural networks (DCNN) is put forward, which consists of a straight channel and a convolution channel. The straight channel is responsible for ensuring the patency of the deep neural networks, and the convolution channel is responsible for learning the deep neural networks. The training of the deep networks is prone to exhibit instability. To this end, the convolution attenuation factor is proposed, which can scale down the convolution channel’s responses. A new pooling method called “dual-pool layer” is proposed to down-sample on the same feature map, which can prevent over fitting train and ensure the consistency of the dimensions on each channel. The proposed algorithm is evaluated on three image datasets CIFAR-10, CIFAR-100 and MNIST. Experimental results indicate that compared with existing deep convolutional neural networks, the depth, stability and accuracy of DCNN are significantly increased.