Counterexample-Guided Data Augmentation
Counterexample-Guided Data Augmentation
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DOI：
10.24963/ijcai.2018/286
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
T. Dreossi;Shromona Ghosh;Xiangyu Yue;K. Keutzer;A. Sangiovanni-Vincentelli;S. Seshia
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文献类型：
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作者：
T. Dreossi;Shromona Ghosh;Xiangyu Yue;K. Keutzer;A. Sangiovanni-Vincentelli;S. Seshia
我们提出了一个新的框架，用于增强数据集的机器学习的基础上的反例。反例是错误分类的例子，对重新训练和改进模型具有重要的性质。我们的框架的关键组件包括一个\textit{反例生成器}，它产生被模型和错误表错误分类的数据项，这是一种存储与错误分类有关的信息的新型数据结构。错误表可用于解释模型的漏洞，并用于有效地生成用于增强的反例。我们通过将该框架与基于深度神经网络的自动驾驶中的对象检测案例研究中的经典增强技术进行比较，展示了该框架的有效性。
We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a \textit{counterexample generator}, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the efficacy of the proposed framework by comparing it to classical augmentation techniques on a case study of object detection in autonomous driving based on deep neural networks.