mlsanityData to 13 Oct 2026

BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty

arXiv 2007.06096 ↗First posted 12 Jul 2020stat.ML cs.LGcited by 7

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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.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Jul 2020.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2.

7 papers.

  1. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videoscited by 12
  2. 2024SUMix: Mixup with Semantic and Uncertain Informationcited by 12
  3. 2022VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimationcited by 9
  4. 2023Introspective Deep Metric Learningcited by 7
  5. 2021Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?cited by 3
  6. 2022An out-of-distribution discriminator based on Bayesian neural network epistemic uncertaintycited by 0
  7. 2024Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detectioncited by 0