Counterexample-Guided Data Augmentation
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 \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.
Cite
Text
Dreossi et al. "Counterexample-Guided Data Augmentation." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/286Markdown
[Dreossi et al. "Counterexample-Guided Data Augmentation." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/dreossi2018ijcai-counterexample/) doi:10.24963/IJCAI.2018/286BibTeX
@inproceedings{dreossi2018ijcai-counterexample,
title = {{Counterexample-Guided Data Augmentation}},
author = {Dreossi, Tommaso and Ghosh, Shromona and Yue, Xiangyu and Keutzer, Kurt and Sangiovanni-Vincentelli, Alberto L. and Seshia, Sanjit A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {2071-2078},
doi = {10.24963/IJCAI.2018/286},
url = {https://mlanthology.org/ijcai/2018/dreossi2018ijcai-counterexample/}
}