  Recent work has studied the reasons for the remarkable performance of deep
neural networks in image classification. We examine batch normalization on the
one hand and the dynamical systems view of residual networks on the other hand.
Our goal is in understanding the notions of stability and smoothness of the
inter-layer propagation of ResNets so as to explain when they contribute to
significantly enhanced performance. We postulate that such stability is of
importance for the trained ResNet to transfer.
