Post-hoc, unsupervised concept-based explanation methods (U-CBEMs) are a promising tool for generating semantic explanations of the decision-making processes in deep neural networks, having applications in both model improvement and understanding. It is vital that the explanation is accurate, or faithful, to the model, yet we identify several limitations of prior faithfulness metrics that inhibit an accurate evaluation; most notably, prior metrics involve only the set of concepts present, ignoring how they may be spatially distributed. We address these limitations with Surrogate Faithfulness (SF), an evaluation method that introduces a spatially-aware surrogate and two novel faithfulness metrics. Using SF, we produce Optimally Faithful (OF) explanations, where concepts are found that maximize faithfulness. Our experiments show that (1) adding spatial-awareness to prior U-CBEMs increases faithfulness in all cases; (2) OF produces significantly more faithful explanations than prior U-CBEMs (30% or higher improvement in error); (3) OF's learned concepts generalize well to out-of-domain data and are more robust to adversarial examples, where prior U-CBEMs struggle.

本文解决了现有解释方法中忠实度评估不足的问题，提出了一种新的评估方法——代理忠实度（SF），以及两个新颖的忠实度度量标准。研究表明，空间感知的引入显著提高了解释的忠实度，最佳忠实度（OF）能在多种情况下提供比以往更为准确的解释，并在处理不同领域的数据和对抗样本时表现出更好的鲁棒性。

面向空间感知和最佳忠实度的基于概念的解释