  In this paper, we present an approach for Recurrent Iterative Gating called
RIGNet. The core elements of RIGNet involve recurrent connections that control
the flow of information in neural networks in a top-down manner, and different
variants on the core structure are considered. The iterative nature of this
mechanism allows for gating to spread in both spatial extent and feature space.
This is revealed to be a powerful mechanism with broad compatibility with
common existing networks. Analysis shows how gating interacts with different
network characteristics, and we also show that more shallow networks with
gating may be made to perform better than much deeper networks that do not
include RIGNet modules.
