Data-driven Learning-enabled Systems are limited by the quality of available training data, particularly when trained offline. For systems that must operate in real-world environments, the space of possible conditions that can occur is vast and difficult to comprehensively predict at design time. Environmental uncertainty arises when run-time conditions diverge from design-time training conditions. To address this problem, automated methods can generate synthetic data to fill in gaps for training and test data coverage. We propose an evolution-based technique to assist developers with uncovering limitations in existing data when previously unseen environmental phenomena are introduced. This technique explores unique contexts for a given environmental condition, with an emphasis on diversity. Synthetic data generated by this technique may be used for two purposes: (1) to assess the robustness of a system to uncertain environmental factors and (2) to improve the system’s robustness. This technique is demonstrated to outperform random and greedy methods for multiple adverse environmental conditions applied to image-processing Deep Neural Networks.

中文翻译：

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恩基

数据驱动的支持学习的系统受到可用训练数据质量的限制，尤其是在离线训练时。对于必须在现实世界环境中运行的系统，可能发生的条件空间是巨大的，并且难以在设计时进行全面预测。当运行时条件与设计时训练条件不同时，就会出现环境不确定性。为了解决这个问题，自动化方法可以生成合成数据来填补训练和测试数据覆盖的空白。我们提出了一种基于进化的技术，以帮助开发人员在引入以前看不见的环境现象时发现现有数据的局限性。这种技术探索给定环境条件的独特背景，强调多样性。通过这种技术生成的合成数据可用于两个目的：（1）评估系统对不确定环境因素的鲁棒性和（2）提高系统的鲁棒性。对于应用于图像处理深度神经网络的多种不利环境条件，该技术被证明优于随机和贪婪方法。