# Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

arXiv: https://arxiv.org/abs/1912.05170 · HTML: https://mlsanity.com/paper/1912.05170/

## Key facts

- Cited by 101 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 101)

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- [Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation](https://mlsanity.com/paper/2207.06214/) (2022)
- [Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion](https://mlsanity.com/paper/2106.09291/) (2021)
- [Adaptive conformal classification with noisy labels](https://mlsanity.com/paper/2309.05092/) (2023)
- [CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning](https://mlsanity.com/paper/2202.05613/) (2022)
- [Neighborhood Collective Estimation for Noisy Label Identification and Correction](https://mlsanity.com/paper/2208.03207/) (2022)
- [Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification](https://mlsanity.com/paper/2204.09371/) (2022)
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- [Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise](https://mlsanity.com/paper/2112.02960/) (2021)
- [Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification](https://mlsanity.com/paper/2302.02070/) (2023)
- [Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction](https://mlsanity.com/paper/2104.11436/) (2021)
- [Analysing the Noise Model Error for Realistic Noisy Label Data](https://mlsanity.com/paper/2101.09763/) (2021)
- [A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?](https://mlsanity.com/paper/2404.01775/) (2024)
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- [Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion](https://mlsanity.com/paper/2207.11327/) (2022)
- [Instance-dependent Label Distribution Estimation for Learning with Label Noise](https://mlsanity.com/paper/2212.08380/) (2022)
- [Unsupervised classification to improve the quality of a bird song recording dataset](https://mlsanity.com/paper/2302.07560/) (2023)
- [Deep learning with noisy labels in medical prediction problems: a scoping review](https://mlsanity.com/paper/2403.13111/) (2024)
- [Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models](https://mlsanity.com/paper/2405.03869/) (2024)
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- [Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features](https://mlsanity.com/paper/2212.09321/) (2022)
- [Slight Corruption in Pre-training Data Makes Better Diffusion Models](https://mlsanity.com/paper/2405.20494/) (2024)

## Abstract (arXiv)

Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the labeling process or difficulty of correctly classifying data, even for the experts. Because of these practical challenges, label noise is a common problem in real-world datasets, and numerous methods to train deep neural networks with label noise are proposed in the literature. Although deep neural networks are known to be relatively robust to label noise, their tendency to overfit data makes them vulnerable to memorizing even random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its adverse effects to train deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, the literature lacks a comprehensive survey of methodologies centered explicitly around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods. Algorithms in the first group aim to estimate the noise structure and use this information to avoid the adverse effects of noisy labels. Differently, methods in the second group try to come up with inherently noise robust algorithms by using approaches like robust losses, regularizers or other learning paradigms.