Emergent Properties of Foveated Perceptual Systems

We introduce foveated perceptual systems, inspired by human biological systems, and examine the impact that this foveation stage has on the nature and robustness of subsequently learned visual representation. Specifically, these \textit{two-stage} perceptual systems first foveate an image, inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN) and trained to perform scene categorization. We find that: 1-- Systems trained on foveated inputs (Foveation-Nets) have similar generalization as networks trained on matched-resource networks without foveated input (Standard-Nets), yet show greater cross-generalization. 2-- Foveation-Nets show higher robustness than Standard-Nets to scotoma (fovea removed) occlusions, driven by the first foveation stage. 3-- Subsequent representations learned in the CNN of Foveation-Nets weigh center information more strongly than Standard-Nets. 4-- Foveation-Nets show less sensitivity to low-spatial frequency information than Standard-Nets. Furthermore, when we added biological and artificial augmentation mechanisms to each system through simulated eye-movements or random cropping and mirroring respectively, we found that these effects were amplified. Taken together, we find evidence that foveated perceptual systems learn a visual representation that is distinct from non-foveated perceptual systems, with implications in generalization, robustness, and perceptual sensitivity. These results provide computational support for the idea that the foveated nature of the human visual system might confer a functional advantage for scene representation.