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

TL;DR

This paper enhances the interpretability of deep neural networks by generalizing layerwise relevance correction techniques, proposing a filtering method tailored to neural network signal trends, and emphasizing the importance of groundtruth interpretability data.

Contribution

It introduces a generalized layerwise error correction method for relevance maps, considering various error types and leveraging groundtruth interpretability information.

Findings

Proposed a filtering technique for relevance signal rectification.

Studied error propagation forms in relevance methods.

Argued for the use of groundtruth interpretability data.

Abstract

The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting relevant regions that contribute significantly to its prediction. A layer-wise amplitude filtering method was previously introduced to improve the quality of heatmaps, performing error corrections by noise-spike suppression. In this study, we generalize the layerwise error correction by considering any identifiable error and assuming there exists a groundtruth interpretable information. The forms of errors propagated through layerwise relevance methods are studied and we propose a filtering technique for interpretability signal rectification taylored to the trend of signal amplitude of the particular neural network used. Finally, we put forth arguments for the use of groundtruth interpretable information.