# Learning with Neighbor Consistency for Noisy Labels

arXiv: https://arxiv.org/abs/2202.02200 · HTML: https://mlsanity.com/paper/2202.02200/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 51 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [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)
- [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)
- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [WebVision Database: Visual Learning and Understanding from Web Data](https://mlsanity.com/paper/1708.02862/) (2017)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Does label smoothing mitigate label noise?](https://mlsanity.com/paper/2003.02819/) (2020)
- [CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise](https://mlsanity.com/paper/1711.07131/) (2017)
- [SELF: Learning to Filter Noisy Labels with Self-Ensembling](https://mlsanity.com/paper/1910.01842/) (2019)
- [Learning to Learn from Noisy Labeled Data](https://mlsanity.com/paper/1812.05214/) (2018)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [Iterative Learning with Open-set Noisy Labels](https://mlsanity.com/paper/1804.00092/) (2018)
- [Multi-Objective Interpolation Training for Robustness to Label Noise](https://mlsanity.com/paper/2012.04462/) (2020)
- [Embedding Propagation: Smoother Manifold for Few-Shot Classification](https://mlsanity.com/paper/2003.04151/) (2020)

## Built on by (most cited first, 30 of 55)

- [Graph Matching with Bi-level Noisy Correspondence](https://mlsanity.com/paper/2212.04085/) (2022)
- [Improving Image Recognition by Retrieving from Web-Scale Image-Text Data](https://mlsanity.com/paper/2304.05173/) (2023)
- [Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels](https://mlsanity.com/paper/2305.19518/) (2023)
- [Vision-Language Models are Strong Noisy Label Detectors](https://mlsanity.com/paper/2409.19696/) (2024)
- [Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement](https://mlsanity.com/paper/2305.12711/) (2023)
- [Mitigating Label Noise through Data Ambiguation](https://mlsanity.com/paper/2305.13764/) (2023)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [A Generative Approach for Wikipedia-Scale Visual Entity Recognition](https://mlsanity.com/paper/2403.02041/) (2024)
- [Regularly Truncated M-estimators for Learning with Noisy Labels](https://mlsanity.com/paper/2309.00894/) (2023)
- [Regroup Median Loss for Combating Label Noise](https://mlsanity.com/paper/2312.06273/) (2023)
- [Generalizable Person Re-identification via Balancing Alignment and Uniformity](https://mlsanity.com/paper/2411.11471/) (2024)
- [Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains](https://mlsanity.com/paper/2402.11039/) (2024)
- [Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning](https://mlsanity.com/paper/2405.05714/) (2024)
- [Dynamic Loss For Robust Learning](https://mlsanity.com/paper/2211.12506/) (2022)
- [Combating Label Noise With A General Surrogate Model For Sample Selection](https://mlsanity.com/paper/2310.10463/) (2023)
- [Data Optimization in Deep Learning: A Survey](https://mlsanity.com/paper/2310.16499/) (2023)
- [Learning with Noisy Labels over Imbalanced Subpopulations](https://mlsanity.com/paper/2211.08722/) (2022)
- [SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels](https://mlsanity.com/paper/2211.11753/) (2022)
- [A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification](https://mlsanity.com/paper/2212.03411/) (2022)
- [Learning with Noisy Labels through Learnable Weighting and Centroid Similarity](https://mlsanity.com/paper/2303.09470/) (2023)
- [Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement](https://mlsanity.com/paper/2505.19675/) (2025)
- [Towards Robust Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2309.11228/) (2023)
- [CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes](https://mlsanity.com/paper/2310.09761/) (2023)
- [Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data](https://mlsanity.com/paper/2504.17474/) (2025)
- [LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration](https://mlsanity.com/paper/2306.11911/) (2023)
- [URL: Combating Label Noise for Lung Nodule Malignancy Grading](https://mlsanity.com/paper/2308.08772/) (2023)
- [Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning](https://mlsanity.com/paper/2310.15533/) (2023)
- [Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs](https://mlsanity.com/paper/2505.01064/) (2025)
- [Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning](https://mlsanity.com/paper/2208.08132/) (2022)
- [Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2211.16847/) (2022)

## Abstract (arXiv)

Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, WebVision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them.