  We present a new technique that explains the output of a CNN-based model
using a combination of GradCAM and LRP methods. Both of these methods produce
visual explanations by highlighting input regions that are important for
predictions. In the new method, the explanation produced by GradCAM is first
processed to remove noises. The processed output is then multiplied elementwise
with the output of LRP. Finally, a Gaussian blur is applied on the product. We
compared the proposed method with GradCAM and LRP on the metrics of
Faithfulness, Robustness, Complexity, Localisation and Randomisation. It was
observed that this method performs better on Complexity than both GradCAM and
LRP and is better than atleast one of them in the other metrics.
