  While some convolutional neural networks (CNNs) have surpassed human visual
abilities in object classification, they often struggle to recognize objects in
images corrupted with different types of common noise patterns, highlighting a
major limitation of this family of models. Recently, it has been shown that
simulating a primary visual cortex (V1) at the front of CNNs leads to small
improvements in robustness to these image perturbations. In this study, we
start with the observation that different variants of the V1 model show gains
for specific corruption types. We then build a new model using an ensembling
technique, which combines multiple individual models with different V1
front-end variants. The model ensemble leverages the strengths of each
individual model, leading to significant improvements in robustness across all
corruption categories and outperforming the base model by 38% on average.
Finally, we show that using distillation, it is possible to partially compress
the knowledge in the ensemble model into a single model with a V1 front-end.
While the ensembling and distillation techniques used here are hardly
biologically-plausible, the results presented here demonstrate that by
combining the specific strengths of different neuronal circuits in V1 it is
possible to improve the robustness of CNNs for a wide range of perturbations.
