The robustness gains observed in the novel CNN families, RetinaNets and EVNets, may scale positively to larger input image sizes due to the multi-stage front-end architecture that simulates early visual processing. Larger images could provide more detailed spatial information, allowing the RetinaBlock and VOneBlock to extract richer features that enhance robustness against common corruptions. The increased resolution may also improve the model's ability to generalize across various datasets, as the biologically-inspired components are designed to mimic the hierarchical processing of visual information akin to the primate visual system.
However, the effectiveness of these robustness gains on out-of-domain datasets would depend on the similarity of the new datasets to the training data. If the new datasets exhibit significant differences in distribution or corruption types, the models may not perform as well. Future research should focus on evaluating these models on diverse out-of-domain datasets to assess their generalization capabilities. Additionally, exploring how the architecture adapts to different input sizes and the impact of varying corruption types will be crucial in understanding the scalability of robustness gains.