# ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

arXiv: https://arxiv.org/abs/1711.11443 · HTML: https://mlsanity.com/paper/1711.11443/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 7 of 7)

- [Gradient Matching for Domain Generalization](https://mlsanity.com/paper/2104.09937/) (2021)
- [A Systematic Study of Bias Amplification](https://mlsanity.com/paper/2201.11706/) (2022)
- [When does dough become a bagel? Analyzing the remaining mistakes on ImageNet](https://mlsanity.com/paper/2205.04596/) (2022)
- [FACET: Fairness in Computer Vision Evaluation Benchmark](https://mlsanity.com/paper/2309.00035/) (2023)
- [ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition](https://mlsanity.com/paper/2502.00156/) (2025)
- [Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding](https://mlsanity.com/paper/2511.08666/) (2025)
- [Generalized Adversarial Distances to Efficiently Discover Classifier Errors](https://mlsanity.com/paper/2102.12844/) (2021)

## Abstract (arXiv)

ConvNets and Imagenet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement combined with the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases question the reliability of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations. The contribution of this study is threefold. We first experimentally demonstrate that the accuracy and robustness of ConvNets measured on Imagenet are vastly underestimated. Next, we show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user. We finally introduce a novel tool for uncovering the undesirable biases learned by a model. These contributions also show that explanations are a valuable tool both for improving our understanding of ConvNets' predictions and for designing more reliable models.