# CTRL: Clustering Training Losses for Label Error Detection

arXiv: https://arxiv.org/abs/2208.08464 · HTML: https://mlsanity.com/paper/2208.08464/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 58 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 30 of 33)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [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)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Early-Learning Regularization Prevents Memorization of Noisy Labels](https://mlsanity.com/paper/2007.00151/) (2020)
- [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)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Normalized Loss Functions for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2006.13554/) (2020)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels](https://mlsanity.com/paper/1905.05040/) (2019)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://mlsanity.com/paper/1711.07131/) (2017)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks](https://mlsanity.com/paper/1903.11680/) (2019)
- [Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels](https://mlsanity.com/paper/2105.04522/) (2021)
- [Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels](https://mlsanity.com/paper/1705.01936/) (2017)
- [Mitigating Memorization of Noisy Labels via Regularization between Representations](https://mlsanity.com/paper/2110.09022/) (2021)

## Built on by (most cited first, 17 of 17)

- [AQuA: A Benchmarking Tool for Label Quality Assessment](https://mlsanity.com/paper/2306.09467/) (2023)
- [DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors](https://mlsanity.com/paper/2305.05738/) (2023)
- [Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization](https://mlsanity.com/paper/2412.18342/) (2024)
- [A Survey of Historical Learning: Learning Models with Learning History](https://mlsanity.com/paper/2303.12992/) (2023)
- [Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning](https://mlsanity.com/paper/2403.06289/) (2024)
- [RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning](https://mlsanity.com/paper/2503.19886/) (2025)
- [Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning](https://mlsanity.com/paper/2311.16481/) (2023)
- [FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning](https://mlsanity.com/paper/2604.19729/) (2026)
- [LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data](https://mlsanity.com/paper/2505.12952/) (2025)
- [EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels](https://mlsanity.com/paper/2510.12687/) (2025)
- [Benchmarking noisy label detection methods](https://mlsanity.com/paper/2510.16211/) (2025)
- [Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation](https://mlsanity.com/paper/2306.13990/) (2023)
- [BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration](https://mlsanity.com/paper/2308.08666/) (2023)
- [PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare](https://mlsanity.com/paper/2403.08197/) (2024)
- [COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare](https://mlsanity.com/paper/2409.09549/) (2024)
- [Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets](https://mlsanity.com/paper/2508.17930/) (2025)
- [Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories](https://mlsanity.com/paper/2602.15154/) (2026)

## Abstract (arXiv)

In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting their label errors can significantly increase their efficacy. We propose a novel framework, called CTRL (Clustering TRaining Losses for label error detection), to detect label errors in multi-class datasets. It detects label errors in two steps based on the observation that models learn clean and noisy labels in different ways. First, we train a neural network using the noisy training dataset and obtain the loss curve for each sample. Then, we apply clustering algorithms to the training losses to group samples into two categories: cleanly-labeled and noisily-labeled. After label error detection, we remove samples with noisy labels and retrain the model. Our experimental results demonstrate state-of-the-art error detection accuracy on both image (CIFAR-10 and CIFAR-100) and tabular datasets under simulated noise. We also use a theoretical analysis to provide insights into why CTRL performs so well.