Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifiers


Kevin Raina (University of Ottawa), Tanya Schmah (University of Ottawa)
The 35th British Machine Vision Conference
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Abstract

Out-of-Distribution (OOD) detection is critical to AI reliability and safety, yet in many practical settings, only a limited amount of training data is available. Bayesian Neural Networks (BNNs) are a promising class of model on which to base OOD detection, because they explicitly represent epistemic (i.e. model) uncertainty. In the small training data regime, BNNs are especially valuable because they can incorporate prior model information. We introduce a new family of Bayesian posthoc OOD scores based on expected logit vectors, and compare 5 Bayesian and 4 deterministic posthoc OOD scores. Experiments on MNIST and CIFAR-10 In-Distributions, with 5000 training samples or less, show that the Bayesian methods outperform corresponding deterministic methods.

Citation

@inproceedings{Raina_2025_BMVC,
author    = {Kevin Raina and Tanya Schmah},
title     = {Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifiers},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_1187/paper.pdf}
}


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