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Title: 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 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.  more » « less
Award ID(s):
1645964
NSF-PAR ID:
10109211
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the ... International Conference on Artificial Intelligence
ISSN:
2521-7860
Page Range / eLocation ID:
2071-2078
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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