  We propose a new layer design by adding a linear gating mechanism to shortcut
connections. By using a scalar parameter to control each gate, we provide a way
to learn identity mappings by optimizing only one parameter. We build upon the
motivation behind Residual Networks, where a layer is reformulated in order to
make learning identity mappings less problematic to the optimizer. The
augmentation introduces only one extra parameter per layer, and provides easier
optimization by making degeneration into identity mappings simpler. We propose
a new model, the Gated Residual Network, which is the result when augmenting
Residual Networks. Experimental results show that augmenting layers provides
better optimization, increased performance, and more layer independence. We
evaluate our method on MNIST using fully-connected networks, showing empirical
indications that our augmentation facilitates the optimization of deep models,
and that it provides high tolerance to full layer removal: the model retains
over 90% of its performance even after half of its layers have been randomly
removed. We also evaluate our model on CIFAR-10 and CIFAR-100 using Wide Gated
ResNets, achieving 3.65% and 18.27% error, respectively.
