# What is the difference between a class activation map and a saliency map for convolutional neural networks?

I am researching attribution methods in computer vision literature to better understand how a CNN model arrives at its predictions. I have come across the terms class activation map and saliency map, and my understanding is that these are approaches that highlight which pixels/regions in the input image have the largest impact on the output prediction. Based off how they are described in articles and lectures I've come across, it seems like these two terms are interchangeable? I wanted to verify if that is indeed correct and if not, what is the difference? Thanks!