Grad CAM

Grad-CAM is a technique used to visualize and interpret the decision-making process of convolutional neural networks (CNNs), primarily focusing on identifying which image regions are most influential in a model's prediction. Current research explores Grad-CAM's application across diverse CNN architectures, including ResNets and more recent models, and investigates improvements to its robustness and faithfulness, addressing issues like gradient saturation and sensitivity to baseline parameters. This work is significant for enhancing the transparency and trustworthiness of AI models, particularly in high-stakes applications like medical image analysis and other domains requiring explainable AI.