From the original paper (http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf http://cnnlocalization.csail.mit.edu/Zhou_Learning

From the original paper (http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Feat...): "A class activation map for a particular category indicates the discriminative image regions used by the CNN to identify that category" So, it's mostly what you said, but the maps you get are specific to the particular trained network you're using; the class activation map tells you, for a given network, for a given image, for a given class, which image regions did that network consider relevant to that class. For learning more about interpreting neural nets I would highly recommend the posts on distill.pub; https://distill.pub/2018/building-blocks/ https://distill.pub/2018/building-blocks/ and https://distill.pub/2019/activation-atlas/ https://distill.pub/2019/activation-atlas/ are probably most pertinent.