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.
Keywords
- Machine Learning: Neural Networks
- Machine Learning: Deep Learning
- Multidisciplinary Topics and Applications: Validation and Verification
- Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
- Computer Vision: Big Data and Large Scale Methods
- Computer Vision: Statistical Methods and Machine Learning
