# Bayesian Autoencoders for Drift Detection in Industrial Environments

arXiv: https://arxiv.org/abs/2107.13249 · HTML: https://mlsanity.com/paper/2107.13249/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning](https://mlsanity.com/paper/1910.14179/) (2019)

## Built on by (most cited first, 6 of 6)

- [Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection](https://mlsanity.com/paper/2202.12653/) (2022)
- [Quantifying the uncertainty of model-based synthetic image quality metrics](https://mlsanity.com/paper/2504.03623/) (2025)
- [Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning](https://mlsanity.com/paper/2110.10038/) (2021)
- [A method to benchmark high-dimensional process drift detection](https://mlsanity.com/paper/2409.03669/) (2024)
- [Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation](https://mlsanity.com/paper/2605.09663/) (2026)
- [Generative Bayesian Filtering for State Estimation](https://mlsanity.com/paper/2607.20521/) (2026)

## Abstract (arXiv)

Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from sensors operating under normal conditions and using the model to detect anomalies. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift); however, the use of Autoencoders to distinguish between different anomalies has not yet been considered. To this end, we first propose the development of Bayesian Autoencoders to quantify epistemic and aleatoric uncertainties. We then test the Bayesian Autoencoder using a real-world industrial dataset for hydraulic condition monitoring. The system is injected with noise and drifts, and we have found the epistemic uncertainty to be less sensitive to sensor perturbations as compared to the reconstruction loss. By observing the reconstructed signals with the uncertainties, we gain interpretable insights, and these uncertainties offer a potential avenue for distinguishing real and virtual drifts.