  Robustness to variations in lighting conditions is a key objective for any
deep vision system. To this end, our paper extends the receptive field of
convolutional neural networks with two residual components, ubiquitous in the
visual processing system of vertebrates: On-center and off-center pathways,
with excitatory center and inhibitory surround; OOCS for short. The on-center
pathway is excited by the presence of a light stimulus in its center but not in
its surround, whereas the off-center one is excited by the absence of a light
stimulus in its center but not in its surround. We design OOCS pathways via a
difference of Gaussians, with their variance computed analytically from the
size of the receptive fields. OOCS pathways complement each other in their
response to light stimuli, ensuring this way a strong edge-detection
capability, and as a result, an accurate and robust inference under challenging
lighting conditions. We provide extensive empirical evidence showing that
networks supplied with the OOCS edge representation gain accuracy and
illumination-robustness compared to standard deep models.
