角点数据描述和检测
Corner case data description and detection
摘要：
作为影响深度学习模型安全性的主要因素，角点案例及其相关检测对于构建安全关键系统的AI质量保证至关重要。一般的角落案例研究涉及两个有趣的话题。一种是通过参数/结构的调整来增强DL模型对角点数据的鲁棒性。另一个是生成新的角点案例，用于模型的再训练和改进。然而，复杂的结构和庞大的参数数量使得DL模型的鲁棒调整不容易，同时不可能生成所有真实世界的角点情况进行DL训练。因此，本文提出了一种简单而新颖的研究，旨在通过一个特定的度量角点情况下的数据检测。该指标是基于在捕获数据行为方面具有优势的惊喜充分性（SA）而开发的。此外，针对角点数据的特点，本文对基于距离的模拟退火算法进行了三种改进，使其更适合于分类应用。通过对MNIST数据和工业数据的实验分析，验证了该方法在角点检测中的可行性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
