Coming Soon - Official implementation of BRACE (BetteR Accuracy from Concept-based Explanation) proposed in Explanation-based Data Augmentation for Image Classification paper. This paper is accepted to NeurIPS 2021.
Existing works have generated explanations for deep neural network decisions to provide insights into model behavior. We observe that these explanations can also be used to identify concepts that caused misclassifications. This allows us to understand the possible limitations of the dataset used to train the model, in particular, the under-represented regions in the dataset. This work proposes a framework that utilizes concept-based explanations to automatically augment the dataset with new images that can cover these under-represented regions to improve the model performance. The framework is able to use the explanations generated by both interpretable classifiers and post-hoc explanations from black-box classifiers. Experiment results demonstrate that the proposed approach improves the accuracy of classifiers compared to state-of-the-art augmentation strategies.
@inproceedings{wickramanayake2021explanation,
title={Explanation-based Data Augmentation for Image Classification},
author={Wickramanayake, Sandareka and Hsu, Wynne and Lee, Mong-Li},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021}
}