  Saliency methods interpret the prediction of a neural network by showing the
importance of input elements for that prediction. A popular family of saliency
methods utilize gradient information. In this work, we empirically show that
two approaches for handling the gradient information, namely positive
aggregation, and positive propagation, break these methods. Though these
methods reflect visually salient information in the input, they do not explain
the model prediction anymore as the generated saliency maps are insensitive to
the predicted output and are insensitive to model parameter randomization.
Specifically for methods that aggregate the gradients of a chosen layer such as
GradCAM++ and FullGrad, exclusively aggregating positive gradients is
detrimental. We further support this by proposing several variants of
aggregation methods with positive handling of gradient information. For methods
that backpropagate gradient information such as LRP, RectGrad, and Guided
Backpropagation, we show the destructive effect of exclusively propagating
positive gradient information.
