  Recent years have seen the introduction of a range of methods for post-hoc
explainability of image classifier predictions. However, these post-hoc
explanations may not always be faithful to classifier predictions, which poses
a significant challenge when attempting to debug models based on such
explanations. To this end, we seek a methodology that can improve the
faithfulness of an explanation method with respect to model predictions which
does not require ground truth explanations. We achieve this through a novel
explanation-driven data augmentation (EDDA) technique that augments the
training data with occlusions inferred from model explanations; this is based
on the simple motivating principle that \emph{if} the explainer is faithful to
the model \emph{then} occluding salient regions for the model prediction should
decrease the model confidence in the prediction, while occluding non-salient
regions should not change the prediction. To verify that the proposed
augmentation method has the potential to improve faithfulness, we evaluate EDDA
using a variety of datasets and classification models. We demonstrate
empirically that our approach leads to a significant increase of faithfulness,
which can facilitate better debugging and successful deployment of image
classification models in real-world applications.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition