EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
This addresses the challenge of debugging image classification models in real-world applications, though it is an incremental improvement on existing explanation methods.
The paper tackles the problem of unfaithful post-hoc explanations in image classifiers by proposing EDDA, an explanation-driven data augmentation technique that improves faithfulness without requiring ground truth explanations, achieving a significant increase in faithfulness across various datasets and models.
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.