With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we investigate to what extent the increasing performance of deep neural networks is impacted by background features? In particular, we focus on background invariance, i.e., accuracy unaffected by switching background features and background influence, i.e., predictive power of background features itself when foreground is masked. We perform experiments with 32 different neural networks ranging from small-size networks to large-scale networks trained with up to one Billion images. Our investigations reveal that increasing expressive power of DNNs leads to higher influence of background features, while simultaneously, increases their ability to make the correct prediction when background features are removed or replaced with a randomly selected texture-based background.
@article{arxiv.2006.14077,
title = {Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks},
author = {Vikash Sehwag and Rajvardhan Oak and Mung Chiang and Prateek Mittal},
journal= {arXiv preprint arXiv:2006.14077},
year = {2020}
}