mlsanityData to 7 Oct 2026

Corner case data description and detection

arXiv 2101.02494 ↗First posted 7 Jan 2021cs.LG cs.AI cs.SEcited by 4

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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.

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

Builds on

15 of 32 references.

References · 15

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  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
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  11. 2018Guiding Deep Learning System Testing using Surprise Adequacycited by 123
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3 more
  1. 2019Towards Corner Case Detection for Autonomous Drivingcited by 39
  2. 2018Testing Deep Neural Networkscited by 36
  3. 2020Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Drivingcited by 21

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023AI Hazard Management: A framework for the systematic management of root causes for AI riskscited by 3
  2. 2022Guiding the retraining of convolutional neural networks against adversarial inputscited by 1
  3. 2023A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurancecited by 0
  4. 2024Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentationcited by 0