Retinotopic Mapping Enhances the Robustness of Convolutional Neural
  Networks
Jean-Nicolas Jérémie Emmanuel Daucé Laurent U Perrinet
Foveated vision, a trait shared by many animals, including humans, has not
been fully utilized in machine learning applications, despite its significant
contributions to biological visual function. This study investigates whether
retinotopic mapping, a critical component of foveated vision, can enhance image
categorization and localization performance when integrated into deep
convolutional neural networks (CNNs). Retinotopic mapping was integrated into
the inputs of standard off-the-shelf convolutional neural networks (CNNs),
which were then retrained on the ImageNet task. As expected, the
logarithmic-polar mapping improved the network's ability to handle arbitrary
image zooms and rotations, particularly for isolated objects. Surprisingly, the
retinotopically mapped network achieved comparable performance in
classification. Furthermore, the network demonstrated improved classification
localization when the foveated center of the transform was shifted. This
replicates a crucial ability of the human visual system that is absent in
typical convolutional neural networks (CNNs). These findings suggest that
retinotopic mapping may be fundamental to significant preattentive visual
processes.