  The Grad-CAM algorithm provides a way to identify what parts of an image
contribute most to the output of a classifier deep network. The algorithm is
simple and widely used for localization of objects in an image, although some
researchers have point out its limitations, and proposed various alternatives.
One of them is Grad-CAM++, that according to its authors can provide better
visual explanations for network predictions, and does a better job at locating
objects even for occurrences of multiple object instances in a single image.
Here we show that Grad-CAM++ is practically equivalent to a very simple
variation of Grad-CAM in which gradients are replaced with positive gradients.
