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• W2803289520 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 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." @default.
• W2803289520 created "2018-06-01" @default.
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• W2803289520 date "2018-05-17" @default.
• W2803289520 modified "2023-09-27" @default.
• W2803289520 title "Counterexample-Guided Data Augmentation" @default.
• W2803289520 cites W2017001763 @default.
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• W2803289520 hasPublicationYear "2018" @default.
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