Regularization of convolutional neural networks using shufflenode

Yihao Chen, Hanli Wang, Yu Long

Abstract:

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.