Counterexample-Guided Data Augmentation

Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia. Counterexample-Guided Data Augmentation. In 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.

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Abstract

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 counterexample generator, which produces data that are misclassified by the model and error tables, a 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 learning.

BibTeX

@inproceedings{dreossi-ijcai18,
  author = {Tommaso Dreossi and Shromona Ghosh and Xiangyu Yue and Kurt Keutzer and Alberto Sangiovanni-Vincentelli and Sanjit A. Seshia},
  title = {Counterexample-Guided Data Augmentation},
  booktitle = {27th International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2018},
  abstract = {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 counterexample generator, 
which produces data that are misclassified by the model and 
error tables, a 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 learning.}
}