Gradient weighted class activation mapping

Another nifty gradient-based method is the gradient weighted class activation map (Grad-CAM). This is useful specifically if you have input images with entities belonging to several output classes and you want to visualize which areas in the input picture your network associates most with a specific output class. This technique leverages the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. In other words, we feed our network an input image and take the output activation map of a convolution layer by weighing every channel of the output (that is, the activation maps) by the gradient of the output class with respect to the channel. This allows us to better utilize the spatial information corresponding to what our network pays...