Class Activation Mapping

Class Activation Mapping (CAM) techniques, particularly Gradient-weighted Class Activation Mapping (Grad-CAM), aim to enhance the interpretability of deep learning models, especially Convolutional Neural Networks (CNNs), by visualizing which parts of an input (e.g., an image) most influence the model's prediction. Current research focuses on improving the reliability and faithfulness of CAM methods, exploring variations like fused multi-class Grad-CAM for more holistic explanations and adapting CAM for diverse architectures beyond CNNs, including Graph Convolutional Networks. This work is significant because it fosters trust and understanding in complex deep learning models used across various fields, from medical diagnosis to agricultural monitoring, by providing visual explanations of their decision-making processes.