Corner case data description and detection

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Ouyang, Tinghui, Vicent Sant Marco, Isobe, Yoshinao, Asoh, Hideki, Oiwa, Yutaka, Seo, Yoshiki
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.03.2021
Subjects
Adequacy
Machine learning
Mathematical models
Parameter robustness
Quality assurance
Retraining
Safety
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Summary: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.
ISSN:2331-8422