Class Activation

Class activation maps (CAMs) are visualization techniques used to interpret the decision-making processes of deep learning models, particularly in computer vision. Current research focuses on improving CAM accuracy and interpretability, exploring variations like Grad-CAM and its extensions, and integrating CAMs with other techniques such as kernel PCA and autoencoders to enhance feature extraction and robustness. This work is significant because it addresses the "black box" nature of deep learning models, fostering trust and enabling better understanding of model behavior in diverse applications ranging from medical image analysis to agricultural technology.