Visualizing Residual Networks
This work provides incremental insights into understanding residual networks, a state-of-the-art architecture in computer vision.
The paper investigates the role of residual skip connections in residual networks, finding through visualization and analysis that they force layers to refine features, confirming existing intuitive knowledge about CNNs.
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.