论文标题
概念发现（SSCCD）的稀疏子空间聚类
Sparse Subspace Clustering for Concept Discovery (SSCCD)
论文作者
论文摘要
概念是更高层次的人类理解的关键基础。可解释的AI（XAI）方法近年来已经显示出巨大的进展，但是，局部归因方法不允许识别样品之间的相干模型行为，因此错过了这一基本组成部分。在这项工作中，我们研究了基于概念的解释，并将概念的新定义作为隐藏特征层的低维子空间。我们在小新生中应用稀疏的子空间聚类来发现这些概念子空间。向前迈进，我们从概念子空间中获得了局部输入（概念）地图的见解，展示了如何量化概念相关性，最后评估概念之间的相似性和可传递性。我们从经验上证明了用于各种不同图像分类任务的概念发现（SSCCD）方法提出的稀疏子空间聚类的合理性。这种方法可以更深入地了解实际模型行为，这些模型行为将隐藏在常规的输入级热图中。
Concepts are key building blocks of higher level human understanding. Explainable AI (XAI) methods have shown tremendous progress in recent years, however, local attribution methods do not allow to identify coherent model behavior across samples and therefore miss this essential component. In this work, we study concept-based explanations and put forward a new definition of concepts as low-dimensional subspaces of hidden feature layers. We novelly apply sparse subspace clustering to discover these concept subspaces. Moving forward, we derive insights from concept subspaces in terms of localized input (concept) maps, show how to quantify concept relevances and lastly, evaluate similarities and transferability between concepts. We empirically demonstrate the soundness of the proposed Sparse Subspace Clustering for Concept Discovery (SSCCD) method for a variety of different image classification tasks. This approach allows for deeper insights into the actual model behavior that would remain hidden from conventional input-level heatmaps.
