# Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification

arXiv: https://arxiv.org/abs/1709.03439 · HTML: https://mlsanity.com/paper/1709.03439/

## Key facts

- Cited by 3 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [When does dough become a bagel? Analyzing the remaining mistakes on ImageNet](https://mlsanity.com/paper/2205.04596/) (2022)
- [Contemplating real-world object classification](https://mlsanity.com/paper/2103.05137/) (2021)
- [Automated Classification of Model Errors on ImageNet](https://mlsanity.com/paper/2401.02430/) (2023)

## Abstract (arXiv)

In a recent decade, ImageNet has become the most notable and powerful benchmark database in computer vision and machine learning community. As ImageNet has emerged as a representative benchmark for evaluating the performance of novel deep learning models, its evaluation tends to include only quantitative measures such as error rate, rather than qualitative analysis. Thus, there are few studies that analyze the failure cases of deep learning models in ImageNet, though there are numerous works analyzing the networks themselves and visualizing them. In this abstract, we qualitatively analyze the failure cases of ImageNet classification results from recent deep learning model, and categorize these cases according to the certain image patterns. Through this failure analysis, we believe that it can be discovered what the final challenges are in ImageNet database, which the current deep learning model is still vulnerable to.