  eXplanation Based Learning (XBL) is an interactive learning approach that
provides a transparent method of training deep learning models by interacting
with their explanations. XBL augments loss functions to penalize a model based
on deviation of its explanations from user annotation of image features. The
literature on XBL mostly depends on the intersection of visual model
explanations and image feature annotations. We present a method to add a
distance-aware explanation loss to categorical losses that trains a learner to
focus on important regions of a training dataset. Distance is an appropriate
approach for calculating explanation loss since visual model explanations such
as Gradient-weighted Class Activation Mapping (Grad-CAMs) are not strictly
bounded as annotations and their intersections may not provide complete
information on the deviation of a model's focus from relevant image regions. In
addition to assessing our model using existing metrics, we propose an
interpretability metric for evaluating visual feature-attribution based model
explanations that is more informative of the model's performance than existing
metrics. We demonstrate performance of our proposed method on three image
classification tasks.
