The RetinaBlock comprises several key components that contribute to its overall effectiveness in enhancing model robustness. These include:
Light-Adaptation Layer: This component normalizes the input based on local mean luminance, which helps the model maintain performance across varying lighting conditions. By adjusting for luminance, the model becomes less sensitive to changes in brightness, thereby improving robustness against brightness-related corruptions.
Difference-of-Gaussian (DoG) Convolutional Layer: The DoG layer simulates the spatial summation and center-surround antagonism characteristic of retinal ganglion cells. This layer enhances feature selectivity by emphasizing edges and contours, which are critical for object recognition. The band-pass filtering effect of the DoG layer helps the model focus on relevant features while suppressing noise, contributing to improved robustness against various corruptions.
Contrast-Normalization Layer: This layer mimics the adaptive processes observed in the lateral geniculate nucleus (LGN) by normalizing activations based on local contrast. By adjusting the model's sensitivity to contrast variations, this component enhances the model's ability to handle contrast-related corruptions, thereby improving overall robustness.
Parallel Pathways for Midget and Parasol Cells: The RetinaBlock processes inputs through separate pathways for midget and parasol cells, which allows the model to capture both color-opponent and achromatic information. This dual processing enhances the model's ability to generalize across different types of visual stimuli, contributing to robustness against a wider range of corruptions.
Together, these components create a multi-faceted approach to early visual processing, allowing the RetinaBlock to effectively enhance the robustness of CNNs against common image corruptions while maintaining a degree of biological plausibility.