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

Adjusting for Dropout Variance in Batch Normalization and Weight Initialization