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

IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification