# Known Unknowns: Uncertainty Quality in Bayesian Neural Networks

arXiv: https://arxiv.org/abs/1612.01251 · HTML: https://mlsanity.com/paper/1612.01251/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring](https://mlsanity.com/paper/2102.00902/) (2021)
- [Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines](https://mlsanity.com/paper/2212.07118/) (2022)
- [Approaching Neural Network Uncertainty Realism](https://mlsanity.com/paper/2101.02974/) (2021)

## Abstract (arXiv)

We evaluate the uncertainty quality in neural networks using anomaly detection. We extract uncertainty measures (e.g. entropy) from the predictions of candidate models, use those measures as features for an anomaly detector, and gauge how well the detector differentiates known from unknown classes. We assign higher uncertainty quality to candidate models that lead to better detectors. We also propose a novel method for sampling a variational approximation of a Bayesian neural network, called One-Sample Bayesian Approximation (OSBA). We experiment on two datasets, MNIST and CIFAR10. We compare the following candidate neural network models: Maximum Likelihood, Bayesian Dropout, OSBA, and --- for MNIST --- the standard variational approximation. We show that Bayesian Dropout and OSBA provide better uncertainty information than Maximum Likelihood, and are essentially equivalent to the standard variational approximation, but much faster.