Counterexample-Guided Data Augmentation

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.

Authors

• Tommaso Dreossi
• Shromona Ghosh
• Xiangyu Yue The Chinese University of Hong Kong
• Kurt Keutzer University of California, Berkeley
• Alberto Sangiovanni-Vincentelli
• Sanjit A. Seshia University of California, Berkeley

Keywords

• Computer Vision: Big Data and Large Scale Methods
• Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
• Computer Vision: Statistical Methods and Machine Learning
• Machine Learning: Deep Learning
• Machine Learning: Neural Networks
• Multidisciplinary Topics and Applications: Validation and Verification