eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although the interactivity of XBL promotes model transparency, XBL requires a huge amount of user interaction and can become expensive as feedback is in the form of detailed annotation rather than simple category labelling which is more common in IML. This expense is exacerbated in high stakes domains such as medical image classification. To reduce the effort and expense of XBL we introduce a new approach that uses two input instances and their corresponding Gradient Weighted Class Activation Mapping (GradCAM) model explanations as exemplary explanations to implement XBL. Using a medical image classification task, we demonstrate that, using minimal human input, our approach produces improved explanations (+0.02, +3%) and achieves reduced classification performance (-0.04, -4%) when compared against a model trained without interactions.

翻译：基于解释的学习（XBL）是一种交互式机器学习（IML）的形式，它通过收集用户对模型解释的反馈来提供模型精化方法。尽管XBL的交互性促进了模型透明度，但XBL需要大量的用户交互，并且可能变得昂贵，因为反馈是以详细注释的形式提供的，而不是IML中更常见的简单类别标注。这种代价在高风险领域（如医学图像分类）中更为严重。为降低XBL的工作量和成本，我们提出了一种新方法，该方法使用两个输入实例及其对应的梯度加权类激活映射（GradCAM）模型解释作为示范性解释来实现XBL。通过医学图像分类任务，我们证明，在最小化人工输入的情况下，与未经交互训练模型相比，我们的方法生成了改进的解释（+0.02，+3%），但分类性能有所下降（-0.04，-4%）。