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.

翻译：由于影响深层学习模型、转角案例和相关检测的安全性的主要因素,影响深层学习模型、转角案例和相关检测的安全性对AI建立安全和安保关键系统的质量保证至关重要。通用转角案例研究涉及两个有趣的议题:一是通过参数/结构的调整加强DL模型的稳健性,通过参数/结构的调整将案件转角数据转角;二是产生新的转角案例,用于模型再培训和改进;然而,复杂的结构和大量的参数使得对DL模型进行稳健的调整不易,同时不可能为DL培训产生所有真实世界转角案例。因此,本文件提议进行一项简单和新的研究,目的是通过具体指标探测转角案例数据。这一指标是针对突如其来的充足性(SA)制定的,具有捕捉数据行为方面的优势。此外,针对转角案例数据的特点,为本文件的分类应用对基于远程的SA进行了三次修改。因此,通过对MNIST的数据和工业数据进行实验分析,核实了拟议中转角案例数据检测方法的可行性和有用性。