  Deep residual networks (ResNets) and their variants are widely used in many
computer vision applications and natural language processing tasks. However,
the theoretical principles for designing and training ResNets are still not
fully understood. Recently, several points of view have emerged to try to
interpret ResNet theoretically, such as unraveled view, unrolled iterative
estimation and dynamical systems view. In this paper, we adopt the dynamical
systems point of view, and analyze the lesioning properties of ResNet both
theoretically and experimentally. Based on these analyses, we additionally
propose a novel method for accelerating ResNet training. We apply the proposed
method to train ResNets and Wide ResNets for three image classification
benchmarks, reducing training time by more than 40% with superior or on-par
accuracy.
