  Residual Network (ResNet) is the state-of-the-art architecture that realizes
successful training of really deep neural network. It is also known that good
weight initialization of neural network avoids problem of vanishing/exploding
gradients. In this paper, simplified models of ResNets are analyzed. We argue
that goodness of ResNet is correlated with the fact that ResNets are relatively
insensitive to choice of initial weights. We also demonstrate how batch
normalization improves backpropagation of deep ResNets without tuning initial
values of weights.
