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Deep convolutional neural networks (DCNNs) have revolutionized computervision and are often advocated as good models of the human visual system.However, there are currently many shortcomings of DCNNs, which preclude them asa model of human vision. There are continuous attempts to use features of thehuman visual system to improve the robustness of neural networks to dataperturbations. We provide a detailed analysis of such bio-inspired models andtheir properties. To this end, we benchmark the robustness of severalbio-inspired models against their most comparable baseline DCNN models. We findthat bio-inspired models tend to be adversarially robust without requiring anyspecial data augmentation. Additionally, we find that bio-inspired models beatadversarially trained models in the presence of more real-world commoncorruptions. Interestingly, we also find that bio-inspired models tend to useboth low and mid-frequency information, in contrast to other DCNN models. Wefind that this mix of frequency information makes them robust to bothadversarial perturbations and common corruptions.

Empirical Advocacy of Bio-inspired Models for Robust Image Recognition