# Robust Learning Under Label Noise With Iterative Noise-Filtering

arXiv: https://arxiv.org/abs/1906.00216 · HTML: https://mlsanity.com/paper/1906.00216/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training](https://mlsanity.com/paper/2205.04723/) (2022)
- [Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels](https://mlsanity.com/paper/2307.05025/) (2023)
- [Training of Neural Networks with Uncertain Data: A Mixture of Experts Approach](https://mlsanity.com/paper/2312.08083/) (2023)

## Abstract (arXiv)

We consider the problem of training a model under the presence of label noise. Current approaches identify samples with potentially incorrect labels and reduce their influence on the learning process by either assigning lower weights to them or completely removing them from the training set. In the first case the model however still learns from noisy labels; in the latter approach, good training data can be lost. In this paper, we propose an iterative semi-supervised mechanism for robust learning which excludes noisy labels but is still able to learn from the corresponding samples. To this end, we add an unsupervised loss term that also serves as a regularizer against the remaining label noise. We evaluate our approach on common classification tasks with different noise ratios. Our robust models outperform the state-of-the-art methods by a large margin. Especially for very large noise ratios, we achieve up to 20 % absolute improvement compared to the previous best model.