Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs

TL;DR

This paper demonstrates that combining multiple V1 brain model variants through ensembling and distillation significantly enhances CNN robustness to various image corruptions, outperforming individual models.

Contribution

It introduces an ensembling approach of different V1 variants and a distillation method to improve CNN robustness against image noise and distortions.

Findings

Ensembling V1 variants improves robustness by 38% on average.

Different V1 variants excel at different corruption types.

Distillation partially compresses ensemble knowledge into a single model.

Abstract

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.