For a machine learning model to generalize well, one needs to ensure that its decisions are supported by meaningful patterns in the input data. A prerequisite is however for the model to be able to explain itself, e.g. by highlighting which input features it uses to support its prediction. Layer-wise Relevance Propagation (LRP) is a technique that brings such explainability and scales to potentially highly complex deep neural networks. It operates by propagating the prediction backward in the neural network, using a set of purposely designed propagation rules. In this chapter, we give a concise introduction to LRP with a discussion of (1) how to implement propagation rules easily and efficiently, (2) how the propagation procedure can be theoretically justified as a ‘deep Taylor decomposition’, (3) how to choose the propagation rules at each layer to deliver high explanation quality, and (4) how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.
CITATION STYLE
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., & Müller, K. R. (2019). Layer-Wise Relevance Propagation: An Overview. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11700 LNCS, pp. 193–209). Springer Verlag. https://doi.org/10.1007/978-3-030-28954-6_10
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