# A Comprehensive Survey on Evidential Deep Learning and Its Applications

arXiv: https://arxiv.org/abs/2409.04720 · HTML: https://mlsanity.com/paper/2409.04720/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 125 of its 211 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 30 of 125)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks](https://mlsanity.com/paper/1602.07868/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data](https://mlsanity.com/paper/2002.11297/) (2020)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://mlsanity.com/paper/2002.06715/) (2020)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://mlsanity.com/paper/1502.05336/) (2015)
- [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014)
- [Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors](https://mlsanity.com/paper/2005.07186/) (2020)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)

## Built on by (most cited first, 25 of 25)

- [Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models](https://mlsanity.com/paper/2410.08611/) (2024)
- [Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion](https://mlsanity.com/paper/2412.18024/) (2024)
- [Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks](https://mlsanity.com/paper/2505.11412/) (2025)
- [Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization](https://mlsanity.com/paper/2503.01468/) (2025)
- [Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R](https://mlsanity.com/paper/2605.19539/) (2026)
- [Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach](https://mlsanity.com/paper/2502.00300/) (2025)
- [MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support](https://mlsanity.com/paper/2511.16625/) (2025)
- [REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization](https://mlsanity.com/paper/2601.17689/) (2026)
- [Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning](https://mlsanity.com/paper/2602.18867/) (2026)
- [A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification](https://mlsanity.com/paper/2605.16383/) (2026)
- [Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning](https://mlsanity.com/paper/2509.00754/) (2025)
- [Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning](https://mlsanity.com/paper/2602.01477/) (2026)
- [The Invisible Gorilla Effect in Out-of-distribution Detection](https://mlsanity.com/paper/2602.20068/) (2026)
- [BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning](https://mlsanity.com/paper/2603.03920/) (2026)
- [Beyond a Single Signal: SPECTREG2, A Unified MultiExpert Anomaly Detector for Unknown Unknowns](https://mlsanity.com/paper/2603.21160/) (2026)
- [Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding](https://mlsanity.com/paper/2604.17927/) (2026)
- [Rethinking Vacuity for OOD Detection in Evidential Deep Learning](https://mlsanity.com/paper/2605.06382/) (2026)
- [Principle-Guided Supervision for Interpretable Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/2605.10984/) (2026)
- [Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier](https://mlsanity.com/paper/2605.22746/) (2026)
- [Robust Fuzzy Multi-view Learning under View Conflict](https://mlsanity.com/paper/2605.24475/) (2026)
- [Courtroom Analogy: New Perspective on Uncertainty-Aware Classification](https://mlsanity.com/paper/2605.25616/) (2026)
- [TMF-RSE: Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty for Lung Severity Scoring](https://mlsanity.com/paper/2607.06356/) (2026)
- [UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks](https://mlsanity.com/paper/2607.06638/) (2026)
- [Uncertainty-Driven Replay Memory for Reinforcement Learning](https://mlsanity.com/paper/2608.29860/) (2026)
- [Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2609.17130/) (2026)

## Abstract (arXiv)

Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However, mainstream uncertainty estimation methods, based on deep ensembling or Bayesian neural networks, generally impose substantial computational overhead. To address this challenge, a novel paradigm called Evidential Deep Learning (EDL) has emerged, providing reliable uncertainty estimation with minimal additional computation in a single forward pass. This survey provides a comprehensive overview of the current research on EDL, designed to offer readers a broad introduction to the field without assuming prior knowledge. Specifically, we first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We further present existing theoretical advancements in EDL from four perspectives: reformulating the evidence collection process, improving uncertainty estimation via OOD samples, delving into various training strategies, and evidential regression networks. Thereafter, we elaborate on its extensive applications across various machine learning paradigms and downstream tasks. In the end, an outlook on future directions for better performances and broader adoption of EDL is provided, highlighting potential research avenues.