Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification

Visual-TCAV is a newly introduced explainability framework that combines local and global explanations for image classification models by utilizing Concept Activation Vectors (CAVs) to generate class-agnostic saliency maps. It enhances the capabilities of traditional TCAV by estimating concept attribution to predictions through a generalization of Integrated Gradients, demonstrating improved alignment with ground truth explanations in evaluations. This framework is significant for practitioners as it provides a more comprehensive understanding of model behavior by linking concept sensitivity directly to specific predictions, facilitating better interpretability in CNNs.