I have few questions, that apperad reading through paper:
Building block of residual network can be viewed as following: data passed to right branch -> convolution, scaling, convolution and in right branch -> identity mapping or convolution, and after that both branches data are summed.
So why it allows to train deep network, escaping network saturation at deep levels? I didn't get the idea from paper. This summation like reminder to network about what has happened few layers ago, reference point? Or just clever regularization?
How amount of right branch layers was picked?
Why we train scale layer on the right branch? According to caffe architecture https://github.com/KaimingHe/deep-residual-networks/blob/master/prototxt/ResNet-50-deploy.prototxt
UPD: This paper http://arxiv.org/abs/1512.03385