Explainability in CNN Models By Means of Z-Scores

David Malmgren-Hansen; Allan Aasbjerg Nielsen; Leif Toudal Pedersen

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arXiv · 2102.05874

Explainability in CNN Models By Means of Z-Scores

David Malmgren-Hansen·Allan Aasbjerg Nielsen·Leif Toudal Pedersen

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Abstract

This paper explores the similarities of output layers in Neural Networks (NNs) with logistic regression to explain importance of inputs by Z-scores. The network analyzed, a network for fusion of Synthetic Aperture Radar (SAR) and Microwave Radiometry (MWR) data, is applied to prediction of arctic sea ice. With the analysis the importance of MWR relative to SAR is found to favor MWR components. Further, as the model represents image features at different scales, the relative importance of these are as well analyzed. The suggested methodology offers a simple and easy framework for analyzing output layer components and can reduce the number of components for further analysis with e.g. common NN visualization methods.

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BibTeX RIS

David Malmgren-Hansen, Allan Aasbjerg Nielsen, Leif Toudal Pedersen. 2021-02-11. Explainability in CNN Models By Means of Z-Scores. https://arxiv.org/abs/2102.05874

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