Residual networks are the current state of the art on ImageNet. Similar work in the direction of utilizing shortcut connections has been done extremely recently with derivatives of residual networks and with highway networks. This work potentially challenges our understanding that CNNs learn layers of local features that are followed by increasingly global features. Through qualitative visualization and empirical analysis, we explore the purpose that residual skip connections serve. Our assessments show that the residual shortcut connections force layers to refine features, as expected. We also provide alternate visualizations that confirm that residual networks learn what is already intuitively known about CNNs in general.
In their study REF , based on a qualitative visualization approach, Chu and his colleagues argued that the residual networks challenged our understanding that CNNs learn layers of local features.
Visualizing Residual Networks
ArXiv
ArXiv
Computer Science