Learning deep features for discriminative localization

Class activation mapping is a technique that uses global average pooling to visualize important regions in images that CNNs use to identify objects. It works by applying global average pooling to activation maps of the last convolutional layer to obtain the importance of each region for predicting the class. The technique was proposed to localize objects for weakly supervised tasks and help understand what CNNs learn from images.