  Contrast is a crucial factor in visual information processing. It is desired
for a visual system - irrespective of being biological or artificial - to
"perceive" the world robustly under large potential changes in illumination. In
this work, we studied the responses of deep neural networks (DNN) to identical
images at different levels of contrast. We analysed the activation of kernels
in the convolutional layers of eight prominent networks with distinct
architectures (e.g. VGG and Inception). The results of our experiments indicate
that those networks with a higher tolerance to alteration of contrast have more
than one convolutional layer prior to the first max-pooling operator. It
appears that the last convolutional layer before the first max-pooling acts as
a mitigator of contrast variation in input images. In our investigation,
interestingly, we observed many similarities between the mechanisms of these
DNNs and biological visual systems. These comparisons allow us to understand
more profoundly the underlying mechanisms of a visual system that is grounded
on the basis of "data-analysis".
