In this article, we explore the limitations of traditional concept-based explanation methods in deep learning models and propose a novel approach called U-ACE (Unified Attention-based Concept Explainer). Our method combines global and local explanation strategies to provide more accurate and interpretable explanations for image classification models. We evaluate the quality of our explanation method using various metrics, including Kendall Tau distance, and demonstrate its superiority over existing methods in terms of both accuracy and interpretability.
Key Points
- Existing concept-based explanation methods are sensitive to the choice of concept set and dataset, leading to unreliable results.
- U-ACE offers a promising alternative by combining global and local explanation strategies, which can handle a wide range of datasets and concepts.
- Our method is evaluated using various metrics, including Kendall Tau distance, and shows superiority over existing methods in terms of both accuracy and interpretability.
- U-ACE provides more accurate explanations than existing methods by considering both local and global contexts.
- The proposed method has important implications for improving the interpretability and reliability of deep learning models in various applications.
Overall, this article offers a valuable contribution to the field of explainable AI by proposing a novel approach that can provide more accurate and interpretable explanations for image classification models. By combining global and local explanation strategies, U-ACE can handle a wide range of datasets and concepts, making it a promising method for improving the interpretability of deep learning models in various applications.
