Multipath Ensemble Convolutional Neural Network

Convolutional neural networks (CNNs) have achieved great success in the field of computer vision in recent years. In order to achieve higher recognition accuracy, the layers of CNNs are generally required to be deeper. However, the increase of CNNs depth will lead to gradient vanishing, which makes insufficient training of network parameters that are close to the input layer and further results in performance degradation. In addition, the simple deepening of layers has limited effects on classification accuracy improvement. Compared with shallow network, the deep network is inherently difficult to be optimized, and thus, converges slowly. To this end, a novel multipath ensemble CNN (ME-CNN) model is proposed in this paper. On one hand, the ME-CNN directly concatenates the low-level with that of high-level output features, and thus, the error gradient can be back propagated through a shorter path to achieve the ultradeep network training. On another, the ME-CNN broadens the network width by increasing the number of feature channels to further improve the network performance. Experimental results on CIFAR and tiny ImageNet datasets verify that the depth of ME-CNN can reach beyond one hundred layers, and the performance gradually increases as the network deepens and widens.