  Deep residual networks (ResNets) made a recent breakthrough in deep learning.
The core idea of ResNets is to have shortcut connections between layers that
allow the network to be much deeper while still being easy to optimize avoiding
vanishing gradients. These shortcut connections have interesting side-effects
that make ResNets behave differently from other typical network architectures.
In this work we use these properties to design a network based on a ResNet but
with parameter sharing and with adaptive computation time. The resulting
network is much smaller than the original network and can adapt the
computational cost to the complexity of the input image.
