REGULARIZATION OF CONVOLUTIONAL NEURAL NETWORKS USING SHUFFLENODE

Convolutional Neural Network (CNN) has recently achieved significant performances for visual computing, and a number of researches are made to explore advanced model structures to solve the problem of over-fitting. In this paper, a regularization technique named ShuffleNode is proposed, which shuffles feature map elements to achieve regularization functions during model training. Specifically, there are two shuffle ways including within-map shuffle and cross-map shuffle, which are suitable to be employed in convolutional layers. The method of within-map shuffle is used to provide the exchange of elements within one feature map, while the cross-map shuffle method offers the opportunity of information sharing across different feature maps. The experimental results on several benchmark image classification datasets demonstrate the efficiency of the proposed method.