  It is widely recognized that the deeper networks or networks with more
feature maps have better performance. Existing studies mainly focus on
extending the network depth and increasing the feature maps of networks. At the
same time, horizontal expansion network (e.g. Inception Model) as an
alternative way to improve network performance has not been fully investigated.
Accordingly, we proposed NeuroTreeNet (NTN), as a new horizontal extension
network through the combination of random forest and Inception Model. Based on
the tree structure, in which each branch represents a network and the root node
features are shared to child nodes, network parameters are effectively reduced.
By combining all features of leaf nodes, even less feature maps achieved better
performance. In addition, the relationship between tree structure and the
performance of NTN was investigated in depth. Comparing to other networks (e.g.
VDSR\_5) with equal magnitude parameters, our model showed preferable
performance in super resolution reconstruction task.
