Class activation mapping (CAM)

Class activation mapping is a method to generate heatmaps of images that show which areas were of high importance in terms of a neural networks for image classification. There are several variations on the method including Score-CAM and Grad-CAM (Gradient Weighted Class Activation Mapping). The heatmaps generated by CAM is a visualization that can be interpreted as telling us where in the image the neural net is (metaphorically) looking to make it's decision, however they do not tell us what particularities it might be looking at. In some cases the heatmaps from CAM can be used not only to inform what pixels were important in the neural network classification process of an image, but also for object localization.