  We propose an enhancement technique of the Class Activation Mapping methods
like Grad-CAM or Excitation Backpropagation, which presents visual explanations
of decisions from CNN-based models. Our idea, called Gradual Extrapolation, can
supplement any method that generates a heatmap picture by sharpening the
output. Instead of producing a coarse localization map highlighting the
important predictive regions in the image, our method outputs the specific
shape that most contributes to the model output. Thus, it improves the accuracy
of saliency maps. Effect has been achieved by gradual propagation of the crude
map obtained in deep layer through all preceding layers with respect to their
activations. In validation tests conducted on a selected set of images, the
proposed method significantly improved the localization detection of the neural
networks' attention. Furthermore, the proposed method is applicable to any deep
neural network model.
