# Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks

arXiv: https://arxiv.org/abs/2006.14077 · HTML: https://mlsanity.com/paper/2006.14077/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Generating High Fidelity Data from Low-density Regions using Diffusion Models](https://mlsanity.com/paper/2203.17260/) (2022)
- [Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations](https://mlsanity.com/paper/2103.12719/) (2021)
- [CLAD: A Contrastive Learning based Approach for Background Debiasing](https://mlsanity.com/paper/2210.02748/) (2022)
- [Background Invariant Classification on Infrared Imagery by Data Efficient Training and Reducing Bias in CNNs](https://mlsanity.com/paper/2201.09144/) (2022)
- [Geometric 2D Scene Graph Generation](https://mlsanity.com/paper/2607.22325/) (2026)

## Abstract (arXiv)

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.