  We show how to adjust for the variance introduced by dropout with corrections
to weight initialization and Batch Normalization, yielding higher accuracy.
Though dropout can preserve the expected input to a neuron between train and
test, the variance of the input differs. We thus propose a new weight
initialization by correcting for the influence of dropout rates and an
arbitrary nonlinearity's influence on variance through simple corrective
scalars. Since Batch Normalization trained with dropout estimates the variance
of a layer's incoming distribution with some inputs dropped, the variance also
differs between train and test. After training a network with Batch
Normalization and dropout, we simply update Batch Normalization's variance
moving averages with dropout off and obtain state of the art on CIFAR-10 and
CIFAR-100 without data augmentation.
