  In this work, we investigate methods to reduce the noise in deep saliency
maps coming from convolutional downsampling. Those methods make the
investigated models more interpretable for gradient-based saliency maps,
computed in hidden layers. We evaluate the faithfulness of those methods using
insertion and deletion metrics, finding that saliency maps computed in hidden
layers perform better compared to both the input layer and GradCAM. We test our
approach on different models trained for image classification on ImageNet1K,
and models trained for tumor detection on Camelyon16 and in-house real-world
digital pathology scans of stained tissue samples. Our results show that the
checkerboard noise in the gradient gets reduced, resulting in smoother and
therefore easier to interpret saliency maps.
