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The practical application of deep neural networks are still limited by theirlack of transparency. One of the efforts to provide explanation for decisionsmade by artificial intelligence (AI) is the use of saliency or heat mapshighlighting relevant regions that contribute significantly to its prediction.A layer-wise amplitude filtering method was previously introduced to improvethe quality of heatmaps, performing error corrections by noise-spikesuppression. In this study, we generalize the layerwise error correction byconsidering any identifiable error and assuming there exists a groundtruthinterpretable information. The forms of errors propagated through layerwiserelevance methods are studied and we propose a filtering technique forinterpretability signal rectification taylored to the trend of signal amplitudeof the particular neural network used. Finally, we put forth arguments for theuse of groundtruth interpretable information.

Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network