Explore deep neural network explanation methods in this 37-minute lecture from the University of Central Florida's CAP6412 course. Delve into key contributions and study details, examining various visual explanation techniques such as saliency maps, scoped rules (Anchors), and SHAP. Investigate a unified representation framework for visual explanations, covering superimposition-based and training data-based methods. Learn about the study methodology, including task design, dataset selection, and model explanations. Analyze results on usability, stability, and privacy risks associated with different explanation approaches. Gain insights into the challenges of explaining complex neural networks and the implications for AI transparency and interpretability.