A study on layer connection strategies in stacked convolutional deep belief networks

Abstract

This paper presents a study on the layer connections in stacked convolutional networks. To this purpose, three layer connection types namely: diverging connection, neighboring connection and full connection have been compared in convolutional deep belief networks (CDBN). The results showed that our proposed full connection could achieve better performance, a lower time and space cost in nearly all conditions compared with the other two strategies. It can be found that full connection strategy combined the features achieved from lower layers well and made a better typical higher layer features.