Abstract:

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.