  Within the last decade, neural network based predictors have demonstrated
impressive - and at times super-human - capabilities. This performance is often
paid for with an intransparent prediction process and thus has sparked numerous
contributions in the novel field of explainable artificial intelligence (XAI).
In this paper, we focus on a popular and widely used method of XAI, the
Layer-wise Relevance Propagation (LRP). Since its initial proposition LRP has
evolved as a method, and a best practice for applying the method has tacitly
emerged, based however on humanly observed evidence alone. In this paper we
investigate - and for the first time quantify - the effect of this current best
practice on feedforward neural networks in a visual object detection setting.
The results verify that the layer-dependent approach to LRP applied in recent
literature better represents the model's reasoning, and at the same time
increases the object localization and class discriminativity of LRP.
