# Improving Label Error Detection and Elimination with Uncertainty Quantification

arXiv: https://arxiv.org/abs/2405.09602 · HTML: https://mlsanity.com/paper/2405.09602/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Are Anchor Points Really Indispensable in Label-Noise Learning?](https://mlsanity.com/paper/1906.00189/) (2019)
- [Does label smoothing mitigate label noise?](https://mlsanity.com/paper/2003.02819/) (2020)
- [Learning with Instance-Dependent Label Noise: A Sample Sieve Approach](https://mlsanity.com/paper/2010.02347/) (2020)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [Learning with Bounded Instance- and Label-dependent Label Noise](https://mlsanity.com/paper/1709.03768/) (2017)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Label Noise Types and Their Effects on Deep Learning](https://mlsanity.com/paper/2003.10471/) (2020)
- [A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?](https://mlsanity.com/paper/2012.03061/) (2020)
- [Whose AI Dream? In search of the aspiration in data annotation](https://mlsanity.com/paper/2203.10748/) (2022)
- [Data Excellence for AI: Why Should You Care](https://mlsanity.com/paper/2111.10391/) (2021)
- [An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis](https://mlsanity.com/paper/2007.07129/) (2020)
- [Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions](https://mlsanity.com/paper/2103.13607/) (2021)

## Built on by (most cited first, 4 of 4)

- [From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets](https://mlsanity.com/paper/2508.06556/) (2025)
- [Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets](https://mlsanity.com/paper/2508.17930/) (2025)
- [Algebraic Multigrid Acceleration for Efficient Label Spreading](https://mlsanity.com/paper/2608.26309/) (2026)
- [Different Corruptions, Different Signals: Uncertainty and Loss in Federated Data Quality](https://mlsanity.com/paper/2609.31454/) (2026)

## Abstract (arXiv)

Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain label represents a good indicator of an erroneous label. However, latest work has built on softmax probabilities to measure self-confidence. In this paper, we argue that -- as softmax probabilities do not reflect a model's predictive uncertainty accurately -- label error detection requires more sophisticated measures of model uncertainty. Therefore, we develop a range of novel, model-agnostic algorithms for Uncertainty Quantification-Based Label Error Detection (UQ-LED), which combine the techniques of confident learning (CL), Monte Carlo Dropout (MCD), model uncertainty measures (e.g., entropy), and ensemble learning to enhance label error detection. We comprehensively evaluate our algorithms on four image classification benchmark datasets in two stages. In the first stage, we demonstrate that our UQ-LED algorithms outperform state-of-the-art confident learning in identifying label errors. In the second stage, we show that removing all identified errors from the training data based on our approach results in higher accuracies than training on all available labeled data. Importantly, besides our contributions to the detection of label errors, we particularly propose a novel approach to generate realistic, class-dependent label errors synthetically. Overall, our study demonstrates that selectively cleaning datasets with UQ-LED algorithms leads to more accurate classifications than using larger, noisier datasets.