# BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty

arXiv: https://arxiv.org/abs/2007.06096 · HTML: https://mlsanity.com/paper/2007.06096/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos](https://mlsanity.com/paper/2311.00469/) (2023)
- [SUMix: Mixup with Semantic and Uncertain Information](https://mlsanity.com/paper/2407.07805/) (2024)
- [VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation](https://mlsanity.com/paper/2210.06028/) (2022)
- [Introspective Deep Metric Learning](https://mlsanity.com/paper/2309.09982/) (2023)
- [Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?](https://mlsanity.com/paper/2104.09493/) (2021)
- [An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty](https://mlsanity.com/paper/2210.10780/) (2022)
- [Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detection](https://mlsanity.com/paper/2401.04933/) (2024)

## Abstract (arXiv)

Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift. We propose a Bayesian framework to obtain reliable uncertainty estimates for deep classifiers. Our approach consists of a plug-in "generator" used to augment the data with an additional class of points that lie on the boundary of the training data, followed by Bayesian inference on top of features that are trained to distinguish these "out-of-distribution" points.