  One of the key problems in computer vision is adaptation: models are too
rigid to follow the variability of the inputs. The canonical computation that
explains adaptation in sensory neuroscience is divisive normalization, and it
has appealing effects on image manifolds. In this work we show that including
divisive normalization in current deep networks makes them more invariant to
non-informative changes in the images. In particular, we focus on U-Net
architectures for image segmentation. Experiments show that the inclusion of
divisive normalization in the U-Net architecture leads to better segmentation
results with respect to conventional U-Net. The gain increases steadily when
dealing with images acquired in bad weather conditions. In addition to the
results on the Cityscapes and Foggy Cityscapes datasets, we explain these
advantages through visualization of the responses: the equalization induced by
the divisive normalization leads to more invariant features to local changes in
contrast and illumination.
