Counterexample-Guided Data Augmentation

TL;DR

This paper introduces a counterexample-guided data augmentation framework that enhances machine learning models by generating misclassified examples to improve training, demonstrated on autonomous driving object detection.

Contribution

The paper proposes a novel counterexample-based data augmentation framework with a new error table data structure for efficient model improvement.

Findings

Outperforms classical augmentation methods in object detection tasks.

Error tables effectively identify model vulnerabilities.

Counterexample generation improves model robustness.

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.