  Explainability of Deep Neural Networks (DNNs) has been garnering increasing
attention in recent years. Of the various explainability approaches,
concept-based techniques stand out for their ability to utilize
human-meaningful concepts instead of focusing solely on individual pixels.
However, there is a scarcity of methods that consistently provide both local
and global explanations. Moreover, most of the methods have no offer to explain
misclassification cases. Considering these challenges, we present a unified
concept-based system for unsupervised learning of both local and global
concepts. Our primary objective is to uncover the intrinsic concepts underlying
each data category by training surrogate explainer networks to estimate the
importance of the concepts. Our experimental results substantiated the efficacy
of the discovered concepts through diverse quantitative and qualitative
assessments, encompassing faithfulness, completeness, and generality.
Furthermore, our approach facilitates the explanation of both accurate and
erroneous predictions, rendering it a valuable tool for comprehending the
characteristics of the target objects and classes.
