# Corner case data description and detection

arXiv: https://arxiv.org/abs/2101.02494 · HTML: https://mlsanity.com/paper/2101.02494/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Guiding Deep Learning System Testing using Surprise Adequacy](https://mlsanity.com/paper/1808.08444/) (2018)
- [DeepMutation: Mutation Testing of Deep Learning Systems](https://mlsanity.com/paper/1805.05206/) (2018)
- [Towards Corner Case Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09184/) (2019)
- [Testing Deep Neural Networks](https://mlsanity.com/paper/1803.04792/) (2018)
- [Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving](https://mlsanity.com/paper/2006.00894/) (2020)

## Built on by (most cited first, 4 of 4)

- [AI Hazard Management: A framework for the systematic management of root causes for AI risks](https://mlsanity.com/paper/2310.16727/) (2023)
- [Guiding the retraining of convolutional neural networks against adversarial inputs](https://mlsanity.com/paper/2207.03689/) (2022)
- [A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurance](https://mlsanity.com/paper/2310.07998/) (2023)
- [Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation](https://mlsanity.com/paper/2404.11266/) (2024)

## Abstract (arXiv)

As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety- and security-critical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes to a simple and novel study aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.