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This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
| Date | Stars |
|---|---|
| 2026-07-31 | 823 |
| 2026-08-06 | 822 |
Today
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# Awesome Uncertainty in Deep learning
<div align="center">
[](https://opensource.org/licenses/MIT)
[](https://awesome.re)
</div>
This repo is a collection of *awesome* papers, codes, books, and blogs about Uncertainty and Deep learning.
:star: Feel free to star and fork. :star:
If you think we missed a paper, please open a pull request or send a message on the corresponding [GitHub discussion](https://github.com/ENSTA-U2IS-AI/awesome-uncertainty-deeplearning/discussions). Tell us where the article was published and when, and send us GitHub and ArXiv links if they are available.
We are also open to any ideas for improvements!
<h2>
Table of Contents
</h2>
- [Awesome Uncertainty in Deep learning](#awesome-uncertainty-in-deep-learning)
- [Papers](#papers)
- [Surveys](#surveys)
- [Theory](#theory)
- [Bayesian-Methods](#bayesian-methods)
- [Ensemble-Methods](#ensemble-methods)
- [Sampling/Dropout-based-Methods](#samplingdropout-based-methods)
- [Post-hoc-Methods/Auxiliary-Networks](#post-hoc-methodsauxiliary-networks)
- [Data-augmentation/Generation-based-methods](#data-augmentationgeneration-based-methods)
- [Output-Space-Modeling/Evidential-deep-learning](#output-space-modelingevidential-deep-learning)
- [Deterministic-Uncertainty-Methods](#deterministic-uncertainty-methods)
- [Quantile-Regression/Predicted-Intervals](#quantile-regressionpredicted-intervals)
- [Conformal Predictions](#conformal-predictions)
- [Calibration/Evaluation-Metrics](#calibrationevaluation-metrics)
- [Misclassification Detection \& Selective Classification](#misclassification-detection--selective-classification)
- [Anomaly-detection and Out-of-Distribution-Detection](#anomaly-detection-and-out-of-distribution-detection)
- [Uncertainty sources & Aleatoric and Epistemic Uncertainty Disentenglement](#uncertainty-sources--aleatoric-and-epistemic-uncertainty-disentenglement)
- [Uncertainty Quantification in Multimodal Models / GenAI](#uncertainty-quantification-in-multimodal-models--genai)
- [Applications](#applications)
- [Classification and Semantic-Segmentation](#classification-and-semantic-segmentation)
- [Regression](#regression)
- [Object detection](#object-detection)
- [Domain adaptation](#domain-adaptation)
- [Semi-supervised and Active Learning](#semi-supervised-and-active-learning)
- [Natural Language Processing](#natural-language-processing)
- [Others](#others)
- [Datasets and Benchmarks](#datasets-and-benchmarks)
- [Libraries](#libraries)
- [Python](#python)
- [PyTorch](#pytorch)
- [JAX](#jax)
- [TensorFlow](#tensorflow)
- [Lectures and tutorials](#lectures-and-tutorials)
- [Books](#books)
- [Other Resources](#other-resources)
# Papers
## Surveys
**Conference**
- Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks [[NeurIPS2024](<https://arxiv.org/abs/2402.19460>) - [[PyTorch]](<https://github.com/bmucsanyi/untangle>)
- A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications [[AISafety Workshop 2020]](<https://arxiv.org/abs/2006.15172>)
**Journal**
- A survey of uncertainty in deep neural networks [[Artificial Intelligence Review 2023]](<https://arxiv.org/abs/2107.03342>) - [[GitHub]](<https://github.com/JakobCode/UncertaintyInNeuralNetworks_Resources>)
- Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation [[TMLR2023]](<https://arxiv.org/abs/2110.03051>)
- A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective [[ACM2021]](<https://dl.acm.org/doi/pdf/10.1145/3477140?casa_token=6fozCYTovlIAAAAA:t5vcjuXCMem1b8iFwaMG4o_YJHTe0wArLtoy9KCbL8Cow0aGEoxSiJans2Kzpm2FSKOg-4ZCDkBa>)
- Ensemble deep learning: A review [[Engineering Applications of AI 2021]](<https://arxiv.org/abs/21Excerpt of 85,610 characters
Read on GitHub
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Olivier Laurent · Université Paris-Saclay
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Youran
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Dylan Bouchard · United States
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Allen Schmaltz
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:78c118b74fe79f60, llm:Repository description and README: 'collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models' and topics include 'uncertainty-estimation', 'deep-learning', 'uncertainty-quantification', 'awesome', 'awesome-resources'.
matched fp:78c118b74fe79f60, llm:Repository description and README: 'collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models' and topics include 'uncertainty-estimation', 'deep-learning', 'uncertainty-quantification', 'awesome', 'awesome-resources'.