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.

eXplanation Based Learning (XBL)是一种交互式学习方法，通过与解释交互来提供一种透明的训练深度学习模型的方法。我们提出了一种在分类损失中添加距离感知解释损失的方法，该方法训练学习者关注训练数据集中的重要区域。我们提出了一个解释能力度量标准，用于评估基于可视特征归因的模型解释，并展示了该方法在三个图像分类任务上的性能。

距离感知的基于解释的学习