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Layer-wise relevance propagation (LRP) is a widely used and powerfultechnique to reveal insights into various artificial neural network (ANN)architectures. LRP is often used in the context of image classification. Theaim is to understand, which parts of the input sample have highest relevanceand hence most influence on the model prediction. Relevance can be traced backthrough the network to attribute a certain score to each input pixel. Relevancescores are then combined and displayed as heat maps and give humans anintuitive visual understanding of classification models. Opening the black boxto understand the classification engine in great detail is essential for domainexperts to gain trust in ANN models. However, there are pitfalls in terms ofmodel-inherent artifacts included in the obtained relevance maps, that caneasily be missed. But for a valid interpretation, these artifacts must not beignored. Here, we apply and revise LRP on various ANN architectures trained asclassifiers on geospatial and synthetic data. Depending on the networkarchitecture, we show techniques to control model focus and give guidance toimprove the quality of obtained relevance maps to separate facts fromartifacts.

Fact or Artifact? Revise Layer-wise Relevance Propagation on various ANN Architectures