# NoiseRank: Unsupervised Label Noise Reduction with Dependence Models

arXiv: https://arxiv.org/abs/2003.06729 · HTML: https://mlsanity.com/paper/2003.06729/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning](https://mlsanity.com/paper/2203.14542/) (2022)
- [Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer](https://mlsanity.com/paper/2207.11971/) (2022)
- [Fine-Grained Classification with Noisy Labels](https://mlsanity.com/paper/2303.02404/) (2023)
- [Learning to Rectify for Robust Learning with Noisy Labels](https://mlsanity.com/paper/2111.04239/) (2021)
- [Intrinsic Self-Supervision for Data Quality Audits](https://mlsanity.com/paper/2305.17048/) (2023)
- [SURE: SUrvey REcipes for building reliable and robust deep networks](https://mlsanity.com/paper/2403.00543/) (2024)
- [MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels](https://mlsanity.com/paper/2103.10869/) (2021)
- [Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement](https://mlsanity.com/paper/2202.12413/) (2022)
- [GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence](https://mlsanity.com/paper/2307.09810/) (2023)
- [Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction](https://mlsanity.com/paper/2103.07641/) (2021)
- [Deep Image Retrieval is not Robust to Label Noise](https://mlsanity.com/paper/2205.11195/) (2022)
- [Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning](https://mlsanity.com/paper/2310.15533/) (2023)
- [CleanPatrick: A Benchmark for Image Data Cleaning](https://mlsanity.com/paper/2505.11034/) (2025)
- [Friends and Foes in Learning from Noisy Labels](https://mlsanity.com/paper/2103.15055/) (2021)
- [Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts](https://mlsanity.com/paper/2101.03713/) (2021)
- [Deep Learning with Label Noise: A Hierarchical Approach](https://mlsanity.com/paper/2205.14299/) (2022)
- [Reliable Mislabel Detection for Video Capsule Endoscopy Data](https://mlsanity.com/paper/2602.06938/) (2026)
- [Variational Rectification Inference for Learning with Noisy Labels](https://mlsanity.com/paper/2603.17255/) (2026)
- [Noise-Aware Framework for Correcting Corrupted Labels](https://mlsanity.com/paper/2606.11695/) (2026)
- [A Data-Centric Framework for Detecting and Correcting Corrupted Labels](https://mlsanity.com/paper/2606.11699/) (2026)

## Abstract (arXiv)

Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, which is not scalable and error-prone. In this paper, we propose NoiseRank, for unsupervised label noise reduction using Markov Random Fields (MRF). We construct a dependence model to estimate the posterior probability of an instance being incorrectly labeled given the dataset, and rank instances based on their estimated probabilities. Our method 1) Does not require supervision from ground-truth labels, or priors on label or noise distribution. 2) It is interpretable by design, enabling transparency in label noise removal. 3) It is agnostic to classifier architecture/optimization framework and content modality. These advantages enable wide applicability in real noise settings, unlike prior works constrained by one or more conditions. NoiseRank improves state-of-the-art classification on Food101-N (~20% noise), and is effective on high noise Clothing-1M (~40% noise).