  Autoencoders are a powerful and versatile tool often used for various
problems such as anomaly detection, image processing and machine translation.
However, their reconstructions are not always trivial to explain. Therefore, we
propose a fast explainability solution by extending the Layer-wise Relevance
Propagation method with the help of Deep Taylor Decomposition framework.
Furthermore, we introduce a novel validation technique for comparing our
explainability approach with baseline methods in the case of missing
ground-truth data. Our results highlight computational as well as qualitative
advantages of the proposed explainability solution with respect to existing
methods.
