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arXiv · 2303.11734

Unlocking Layer-wise Relevance Propagation for Autoencoders

Kenyu Kobayashi·Renata Khasanova·Arno Schneuwly·Felix Schmidt·Matteo Casserini
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Abstract

Autoencoders are a powerful and versatile tool often used for various problems such as anomaly detection, image processing and machine translation. However, their reconstructions are not always trivial to explain. Therefore, we propose a fast explainability solution by extending the Layer-wise Relevance Propagation method with the help of Deep Taylor Decomposition framework. Furthermore, we introduce a novel validation technique for comparing our explainability approach with baseline methods in the case of missing ground-truth data. Our results highlight computational as well as qualitative advantages of the proposed explainability solution with respect to existing methods.

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Kenyu Kobayashi, Renata Khasanova, Arno Schneuwly, Felix Schmidt, Matteo Casserini. 2023-03-21. Unlocking Layer-wise Relevance Propagation for Autoencoders. https://arxiv.org/abs/2303.11734

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