  Gaining insight into how deep convolutional neural network models perform
image classification and how to explain their outputs have been a concern to
computer vision researchers and decision makers. These deep models are often
referred to as black box due to low comprehension of their internal workings.
As an effort to developing explainable deep learning models, several methods
have been proposed such as finding gradients of class output with respect to
input image (sensitivity maps), class activation map (CAM), and Gradient based
Class Activation Maps (Grad-CAM). These methods under perform when localizing
multiple occurrences of the same class and do not work for all CNNs. In
addition, Grad-CAM does not capture the entire object in completeness when used
on single object images, this affect performance on recognition tasks. With the
intention to create an enhanced visual explanation in terms of visual
sharpness, object localization and explaining multiple occurrences of objects
in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo:
http://35.238.22.135:5000/}, a technique that combines methods from two other
recent techniques---SMOOTHGRAD and Grad-CAM++. Our Smooth Grad-CAM++ technique
provides the capability of either visualizing a layer, subset of feature maps,
or subset of neurons within a feature map at each instance at the inference
level (model prediction process). After experimenting with few images, Smooth
Grad-CAM++ produced more visually sharp maps with better localization of
objects in the given input images when compared with other methods.
