On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
This addresses robustness issues in image classification for computer vision applications, but it is incremental as it builds on existing neural network architectures.
The paper tackled the problem of robustness to lighting variations in deep vision systems by extending convolutional neural networks with on-center and off-center pathways (OOCS) for edge detection, resulting in improved accuracy and illumination-robustness compared to standard models.
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.