mlsanityData to 7 Oct 2026

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

arXiv 2202.08985 ↗First posted 18 Feb 2022cs.LGcited by 15

Not on the map. Why?

Abstract (arXiv)

We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the intermediate layers of a dropout BNN can fail due to the distance metric used. We introduce an alternative approach to measuring embedding uncertainty, justify its use theoretically, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 18 Feb 2022.

Builds on

27 of 40 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  6. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  7. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  8. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  9. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  10. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  11. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  12. 2018Deep Learning for Classical Japanese Literaturecited by 415
15 more
  1. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  2. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  3. 2019Probabilistic Face Embeddingscited by 171
  4. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 164
  5. 2021Probabilistic Embeddings for Cross-Modal Retrievalcited by 150
  6. 2020Data Uncertainty Learning in Face Recognitioncited by 134
  7. 2017Malware Detection by Eating a Whole EXEcited by 114
  8. 2020SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustnesscited by 111
  9. 2018Modeling Uncertainty with Hedged Instance Embeddingcited by 52
  10. 2020A Survey of Machine Learning Methods and Challenges for Windows Malware Classificationcited by 33
  11. 2020Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformerscited by 31
  12. 2017Distance-based Confidence Score for Neural Network Classifierscited by 30
  13. 2018Representing smooth functions as compositions of near-identity functions with implications for deep network optimizationcited by 15
  14. 2015Dropout as a Bayesian Approximation: Appendixcited by 13
  15. 2018The WiLI benchmark dataset for written language identificationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 4; 2025: 4; 2026: 4.

15 papers.

Sort:

By area:

  1. 2024Learning with Mixture of Prototypes for Out-of-Distribution Detectioncited by 43
  2. 2024Pedestrian Attribute Recognition as Label-balanced Multi-label Learningcited by 8
  3. 2022Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Trainingcited by 4
  4. 2023Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignmentcited by 4
  5. 2024Living off the Analyst: Harvesting Features from Yara Rules for Malware Detectioncited by 3
  6. 2024Focused Active Learning for Histopathological Image Classificationcited by 2
  7. 2025On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysiscited by 1
  8. 2026Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Modelscited by 1
  9. 2023Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patientscited by 0
  10. 2025Logit Disagreement: OoD Detection with Bayesian Neural Networkscited by 0
  11. 2025Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifierscited by 0
  12. 2025MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classificationcited by 0
3 more
  1. 2026Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Modelscited by 0
  2. 2026SoK: AI-Augmented Binary Reversingcited by 0
  3. 2026Uncertainty quantification for trustworthy deep learning: Methods and measurescited by 0