Data to 7 Oct 2026

Evidential Deep Learning to Quantify Classification Uncertainty

arXiv 1806.01768 ↗First posted 5 Jun 2018cs.LG stat.MLcited by 728

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Abstract (arXiv)

Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of out-of-distribution queries and endurance against adversarial perturbations.

Key facts. Cited by 728 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 53; 2022: 85; 2023: 114; 2024: 156; 2025: 155; 2026: 164.

728 papers.

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2021A Survey of Uncertainty in Deep Neural Networkscited by 675
2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
2021DEUP: Direct Epistemic Uncertainty Predictioncited by 102
2021Evidential Deep Learning for Open Set Action Recognitioncited by 96
2021Trusted Multi-View Classificationcited by 94
2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
2024VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimationcited by 71
2022Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisationcited by 65

38 more

2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
2023Uncertainty Estimation by Fisher Information-based Evidential Deep Learningcited by 58
2021Trustworthy Long-Tailed Classificationcited by 51
2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policiescited by 49
2022Sample-dependent Adaptive Temperature Scaling for Improved Calibrationcited by 48
2023Credal Bayesian Deep Learningcited by 45
2024Reliable Conflictive Multi-View Learningcited by 45
2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
2021Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentationcited by 40
2022The Unreasonable Effectiveness of Deep Evidential Regressioncited by 40
2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
2022What Makes Graph Neural Networks Miscalibrated?cited by 39
2026ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrievalcited by 39
2022Expanding Low-Density Latent Regions for Open-Set Object Detectioncited by 38
2023On Second-Order Scoring Rules for Epistemic Uncertainty Quantificationcited by 38
2024Cascade Reward Sampling for Efficient Decoding-Time Alignmentcited by 38
2022Nonparametric Uncertainty Quantification for Single Deterministic Neural Networkcited by 36
2021Uncertainty-Aware Machine Translation Evaluationcited by 35
2023Towards Calibrated Robust Fine-Tuning of Vision-Language Modelscited by 35
2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?cited by 34
2022Discovering and forecasting extreme events via active learning in neural operatorscited by 32
2023A comprehensive survey on deep active learning in medical image analysiscited by 32
2022Post-hoc Uncertainty Learning using a Dirichlet Meta-Modelcited by 31
2024COME: Test-time adaption by Conservatively Minimizing Entropycited by 31
2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
2024Credal Learning Theorycited by 30
2023Learn to Accumulate Evidence from All Training Samples: Theory and Practicecited by 28
2022RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustnesscited by 27
2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
2022Towards Open Set Video Anomaly Detectioncited by 26
2023Open Set Action Recognition via Multi-Label Evidential Learningcited by 26
2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
2025Estimating LLM Uncertainty with Evidencecited by 26
2023ELFNet: Evidential Local-global Fusion for Stereo Matchingcited by 25
2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
2022Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcitycited by 24

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