SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations

Abstract

Explanations of neural networks predictions are a necessity for deploying neural networks in safety critical domains. Several methods were developed which identify most relevant input features, such as sensitivity analysis and layer-wise relevance propagation (LRP). It has been shown that the noise in the explanations from the sensitivity analysis can be reduced by averaging over noisy input images, a method referred to as SmoothGrad. We investigate the application of the same principle to LRP and find that it smooths the resulting relevance function leading to improved explanations. Moreover, it can be applied for restoring the correct label of adversarial examples.