  Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard
for various challenging machine learning problems, e.g., image classification,
natural language processing or human action recognition. Although these methods
perform impressively well, they have a significant disadvantage, the lack of
transparency, limiting the interpretability of the solution and thus the scope
of application in practice. Especially DNNs act as black boxes due to their
multilayer nonlinear structure. In this paper we introduce a novel methodology
for interpreting generic multilayer neural networks by decomposing the network
classification decision into contributions of its input elements. Although our
focus is on image classification, the method is applicable to a broad set of
input data, learning tasks and network architectures. Our method is based on
deep Taylor decomposition and efficiently utilizes the structure of the network
by backpropagating the explanations from the output to the input layer. We
evaluate the proposed method empirically on the MNIST and ILSVRC data sets.
