Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning

TL;DR

This paper enhances the explainability of Bayesian Autoencoders in sensor networks by introducing a coalitional approach that improves explanation quality, addressing high correlation issues in predictions.

Contribution

It proposes a novel coalitional Bayesian Autoencoder method to improve explanation quality and mitigate misleading explanations caused by high correlation in predictions.

Findings

Coalitional BAE improves explanation quality in sensor data.

High correlation in BAE explanations can be misleading.

The method outperforms standard BAE in explanation metrics.

Abstract

This paper aims to improve the explainability of Autoencoder's (AE) predictions by proposing two explanation methods based on the mean and epistemic uncertainty of log-likelihood estimate, which naturally arise from the probabilistic formulation of the AE called Bayesian Autoencoders (BAE). To quantitatively evaluate the performance of explanation methods, we test them in sensor network applications, and propose three metrics based on covariate shift of sensors : (1) G-mean of Spearman drift coefficients, (2) G-mean of sensitivity-specificity of explanation ranking and (3) sensor explanation quality index (SEQI) which combines the two aforementioned metrics. Surprisingly, we find that explanations of BAE's predictions suffer from high correlation resulting in misleading explanations. To alleviate this, a "Coalitional BAE" is proposed, which is inspired by agent-based system theory. Our comprehensive experiments on publicly available condition monitoring datasets demonstrate the improved quality of explanations using the Coalitional BAE.