  Loss-gradients are used to interpret the decision making process of deep
learning models. In this work, we evaluate loss-gradient based attribution
methods by occluding parts of the input and comparing the performance of the
occluded input to the original input. We observe that the occluded input has
better performance than the original across the test dataset under certain
conditions. Similar behaviour is observed in sound and image recognition tasks.
We explore different loss-gradient attribution methods, occlusion levels and
replacement values to explain the phenomenon of performance improvement under
occlusion.
