Keywords: convolutional networks, distribution shift, push-pull inhibition, robustness
TL;DR: An empirical study of application of visual inhibition in convolutional networks to strengthen their robustness to distribution shifts
Abstract: Convolutional neural networks have been shown to suffer from distribution shifts in the test data, for instance caused by the so called common corruptions and perturbations. Test images can contain noise, digital transformations, and blur that were not present in the training data, negatively impacting the performance of trained models. Humans experience much stronger robustness to noise and visual distortions than deep networks. In this work, we explore the effectiveness of a neuronal response inhibition mechanism, called push-pull, observed in the early part of the visual system, to increase the robustness of deep convolutional networks. We deploy a Push-Pull inhibition layer as a replacement of the initial convolutional layers (input layer and in the first block of residual and dense architectures) of standard convolutional networks for image classification. We show that the Push-Pull inhibition component increases the robustness of standard networks for image classification to distribution shifts on the CIFAR10-C and CIFAR10-P test sets.