  Understanding neural networks is becoming increasingly important. Over the
last few years different types of visualisation and explanation methods have
been proposed. However, none of them explicitly considered the behaviour in the
presence of noise and distracting elements. In this work, we will show how
noise and distracting dimensions can influence the result of an explanation
model. This gives a new theoretical insights to aid selection of the most
appropriate explanation model within the deep-Taylor decomposition framework.
