  Training very deep neural networks is still an extremely challenging task.
The common solution is to use shortcut connections and normalization layers,
which are both crucial ingredients in the popular ResNet architecture. However,
there is strong evidence to suggest that ResNets behave more like ensembles of
shallower networks than truly deep ones. Recently, it was shown that deep
vanilla networks (i.e. networks without normalization layers or shortcut
connections) can be trained as fast as ResNets by applying certain
transformations to their activation functions. However, this method (called
Deep Kernel Shaping) isn't fully compatible with ReLUs, and produces networks
that overfit significantly more than ResNets on ImageNet. In this work, we
rectify this situation by developing a new type of transformation that is fully
compatible with a variant of ReLUs -- Leaky ReLUs. We show in experiments that
our method, which introduces negligible extra computational cost, achieves
validation accuracies with deep vanilla networks that are competitive with
ResNets (of the same width/depth), and significantly higher than those obtained
with the Edge of Chaos (EOC) method. And unlike with EOC, the validation
accuracies we obtain do not get worse with depth.

Machine Learning;Machine Learning