Counterexample-Guided Data Augmentation
By Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia. 2018
68 citations recorded by the source corpus.
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.
Subfields: Natural Language Processing
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In Natural Language Processing
- Varun Prashant Gangal
- Eduard H. Hovy
- Teruko Mitamura
- Soroush Vosoughi
- Jason Wei
- Steven Y. Feng
- Sarath Chandar
- Alberto L. Sangiovanni-Vincentelli
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, background): “, 2021), augmenting misclassified examples (Dreossi et al., 2018), BERT cross-encoder”
- Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data (2019, background): “Counterexamples are considered to help data augmentation in [11].”
- Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations (2019, unknown): “introduced a counterexample-guided data augmentation framework [18].”