mlsanityData to 7 Oct 2026

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples

arXiv 2010.10474 ↗First posted 20 Oct 2020cs.LG cs.AIcited by 40

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Abstract (arXiv)

Among existing uncertainty estimation approaches, Dirichlet Prior Network (DPN) distinctly models different predictive uncertainty types. However, for in-domain examples with high data uncertainties among multiple classes, even a DPN model often produces indistinguishable representations from the out-of-distribution (OOD) examples, compromising their OOD detection performance. We address this shortcoming by proposing a novel loss function for DPN to maximize the textitrepresentation gap between in-domain and OOD examples. Experimental results demonstrate that our proposed approach consistently improves OOD detection performance.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 8; 2024: 11; 2025: 3; 2026: 2.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  3. 2021Provable Guarantees for Understanding Out-of-distribution Detectioncited by 78
  4. 2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
  5. 2023Uncertainty Estimation by Fisher Information-based Evidential Deep Learningcited by 58
  6. 2022Post-hoc Uncertainty Learning using a Dirichlet Meta-Modelcited by 31
  7. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  8. 2021Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detectioncited by 23
  9. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  10. 2024Uncertainty Estimation by Density Aware Evidential Deep Learningcited by 22
  11. 2023From Global to Local: Multi-scale Out-of-distribution Detectioncited by 18
  12. 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?cited by 17
28 more
  1. 2021Being a Bit Frequentist Improves Bayesian Neural Networkscited by 15
  2. 2024Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modelingcited by 14
  3. 2021Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributionscited by 13
  4. 2022Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptationcited by 13
  5. 2023Uncertainty Estimation for Molecules: Desiderata and Methodscited by 11
  6. 2022Out-Of-Distribution Detection In Unsupervised Continual Learningcited by 10
  7. 2024A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learningcited by 9
  8. 2024Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detectioncited by 9
  9. 2021Deep Classifiers with Label Noise Modeling and Distance Awarenesscited by 8
  10. 2024Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Modelscited by 6
  11. 2025Probabilistic Machine Learning for Noisy Labels in Earth Observationcited by 6
  12. 2021Out-of-distribution detection in satellite image classificationcited by 4
  13. 2023dugMatting: Decomposed-Uncertainty-Guided Mattingcited by 4
  14. 2025Uncertainty-Aware Deep Learning for Wildfire Danger Forecastingcited by 4
  15. 2021Distributional Shifts in Automated Diabetic Retinopathy Screeningcited by 3
  16. 2022TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantificationcited by 3
  17. 2023Likelihood Annealing: Fast Calibrated Uncertainty for Regressioncited by 3
  18. 2023Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimatescited by 2
  19. 2023A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detectioncited by 2
  20. 2024Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Scorecited by 2
  21. 2024WildFit: Autonomous In-situ Model Adaptation for Resource-Constrained IoT Systemscited by 2
  22. 2021On the Importance of Regularisation & Auxiliary Information in OOD Detectioncited by 0
  23. 2022Simple Calibration via Geodesic Kernelscited by 0
  24. 2023Efficient IoT Inference via Context-Awarenesscited by 0
  25. 2024Detecting Out-of-Distribution Samples via Conditional Distribution Entropy with Optimal Transportcited by 0
  26. 2025ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertaintycited by 0
  27. 2026Uncertainty quantification for trustworthy deep learning: Methods and measurescited by 0
  28. 2026Uncertainty-Aware Consistency Distillation for Few-Step Video Generationcited by 0