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A Simple and Effective Method for Uncertainty Quantification and OOD Detection

arXiv 2508.00754 ↗First posted 1 Aug 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Bayesian neural networks and deep ensemble methods have been proposed for uncertainty quantification; however, they are computationally intensive and require large storage. By utilizing a single deterministic model, we can solve the above issue. We propose an effective method based on feature space density to quantify uncertainty for distributional shifts and out-of-distribution (OOD) detection. Specifically, we leverage the information potential field derived from kernel density estimation to approximate the feature space density of the training set. By comparing this density with the feature space representation of test samples, we can effectively determine whether a distributional shift has occurred. Experiments were conducted on a 2D synthetic dataset (Two Moons and Three Spirals) as well as an OOD detection task (CIFAR-10 vs. SVHN). The results demonstrate that our method outperforms baseline models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 1 Aug 2025.

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References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2016Wide Residual Networkscited by 3,070
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  7. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  8. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  9. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  10. 2017Deep Bayesian Active Learning with Image Datacited by 819
  11. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  12. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
12 more
  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  2. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  3. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  4. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  5. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  6. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  7. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  8. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 81
  9. 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
  10. 2018Understanding Autoencoders with Information Theoretic Conceptscited by 29
  11. 2023Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shiftscited by 12
  12. 2024Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methodscited by 2

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