Khalid N. Alharbi

Explainable AI for Deep Visual Recognition: Evaluation, Methods, and Open Challenges

This review explores the key techniques for explainability in deep visual recognition, including model-agnostic methods such as LIME and SHAP, model-specific approaches like saliency maps and feature visualization, and intrinsically interpretable models like decision trees and rule-based systems.