论文标题
反馈类型对解释性互动学习的影响
Impact of Feedback Type on Explanatory Interactive Learning
论文作者
论文摘要
解释性互动学习（XIL）收集了有关视觉模型解释的用户反馈，以实现基于人类的交互式学习方案。不同的用户反馈类型将对用户体验以及收集反馈相关的成本产生不同的影响，因为不同的反馈类型涉及不同级别的图像注释。尽管XIL已用于改善多个域中的分类性能，但不同的用户反馈类型对模型性能和解释精度的影响尚未得到很好的研究。为了指导未来的XIL工作，我们比较了图像分类任务中两种不同用户反馈类型的有效性：（1）指示算法忽略某些虚假图像特征，以及（2）指示算法专注于某些有效的图像特征。我们使用基于梯度加权类激活映射（GARGCAM）XIL模型的解释来支持这两种反馈类型。我们表明，与用户反馈相比，识别和注释的虚假图像特征与用户反馈相比，该模型会发现出色的分类和解释精度，该功能告诉模型专注于有效的图像特征。
Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario. Different user feedback types will have different impacts on user experience and the cost associated with collecting feedback since different feedback types involve different levels of image annotation. Although XIL has been used to improve classification performance in multiple domains, the impact of different user feedback types on model performance and explanation accuracy is not well studied. To guide future XIL work we compare the effectiveness of two different user feedback types in image classification tasks: (1) instructing an algorithm to ignore certain spurious image features, and (2) instructing an algorithm to focus on certain valid image features. We use explanations from a Gradient-weighted Class Activation Mapping (GradCAM) based XIL model to support both feedback types. We show that identifying and annotating spurious image features that a model finds salient results in superior classification and explanation accuracy than user feedback that tells a model to focus on valid image features.
