  While modern convolutional neural networks achieve outstanding accuracy on
many image classification tasks, they are, compared to humans, much more
sensitive to image degradation. Here, we describe a variant of Batch
Normalization, LocalNorm, that regularizes the normalization layer in the
spirit of Dropout while dynamically adapting to the local image intensity and
contrast at test-time. We show that the resulting deep neural networks are much
more resistant to noise-induced image degradation, improving accuracy by up to
three times, while achieving the same or slightly better accuracy on
non-degraded classical benchmarks. In computational terms, LocalNorm adds
negligible training cost and little or no cost at inference time, and can be
applied to already-trained networks in a straightforward manner.
