# Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/1912.05651 · HTML: https://mlsanity.com/paper/1912.05651/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 35)

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- [Model Selection for Bayesian Autoencoders](https://mlsanity.com/paper/2106.06245/) (2021)
- [Laplacian Autoencoders for Learning Stochastic Representations](https://mlsanity.com/paper/2206.15078/) (2022)
- [Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications](https://mlsanity.com/paper/2107.11750/) (2021)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes](https://mlsanity.com/paper/2302.04534/) (2023)
- [Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach](https://mlsanity.com/paper/2102.03151/) (2021)
- [CVAD: A generic medical anomaly detector based on Cascade VAE](https://mlsanity.com/paper/2110.15811/) (2021)
- [Decoder ensembling for learned latent geometries](https://mlsanity.com/paper/2408.07507/) (2024)
- [Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection](https://mlsanity.com/paper/2107.13304/) (2021)
- [Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection](https://mlsanity.com/paper/2202.12653/) (2022)
- [Leveraging variational autoencoders for multiple data imputation](https://mlsanity.com/paper/2209.15321/) (2022)
- [Do Bayesian Variational Autoencoders Know What They Don't Know?](https://mlsanity.com/paper/2212.14272/) (2022)
- [Can We Ignore Labels In Out of Distribution Detection?](https://mlsanity.com/paper/2504.14704/) (2025)
- [Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels](https://mlsanity.com/paper/2504.19816/) (2025)
- [Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning](https://mlsanity.com/paper/2210.05582/) (2022)
- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [Vacant Holes for Unsupervised Detection of the Outliers in Compact Latent Representation](https://mlsanity.com/paper/2306.09646/) (2023)
- [Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions](https://mlsanity.com/paper/2310.16277/) (2023)
- [A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective](https://mlsanity.com/paper/2405.03052/) (2024)
- [Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching](https://mlsanity.com/paper/2412.18703/) (2024)
- [MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction](https://mlsanity.com/paper/2304.00709/) (2023)
- [Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks](https://mlsanity.com/paper/2312.00232/) (2023)

## Abstract (arXiv)

Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this has recently motivated the development of methods to detect such out-of-distribution (OoD) inputs, a robust solution is still lacking. We propose a new probabilistic, unsupervised approach to this problem based on a Bayesian variational autoencoder model, which estimates a full posterior distribution over the decoder parameters using stochastic gradient Markov chain Monte Carlo, instead of fitting a point estimate. We describe how information-theoretic measures based on this posterior can then be used to detect OoD inputs both in input space and in the model's latent space. We empirically demonstrate the effectiveness of our proposed approach.