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.

翻译：解释学习（eXplanation Based Learning, XBL）是一种交互式学习方法，通过与模型解释进行交互，提供了一种透明的深度模型训练方式。XBL通过增强损失函数，根据模型解释与用户对图像特征标注的偏差来惩罚模型。现有关于XBL的研究主要依赖视觉模型解释与图像特征标注的交集。我们提出了一种方法，在分类损失中引入距离感知的解释损失，使训练模型能够聚焦于训练数据集的关键区域。由于视觉模型解释（例如梯度加权类激活映射，Grad-CAMs）并非严格受限于标注边界，其交集可能无法完整反映模型关注区域与相关图像区域之间的偏差，因此距离是计算解释损失的合适方法。除使用现有指标评估模型外，我们提出了一种可解释性指标，用于评估基于视觉特征归因的模型解释，该指标比现有指标更能反映模型的性能。我们在三个图像分类任务上展示了所提方法的性能。