# A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?

arXiv: https://arxiv.org/abs/2012.03061 · HTML: https://mlsanity.com/paper/2012.03061/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 25 of 25)

- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [Transfer and Marginalize: Explaining Away Label Noise with Privileged Information](https://mlsanity.com/paper/2202.09244/) (2022)
- [Exploring the Robustness of In-Context Learning with Noisy Labels](https://mlsanity.com/paper/2404.18191/) (2024)
- [Noisy Label Processing for Classification: A Survey](https://mlsanity.com/paper/2404.04159/) (2024)
- [Compressing Features for Learning with Noisy Labels](https://mlsanity.com/paper/2206.13140/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [Probabilistic Machine Learning for Noisy Labels in Earth Observation](https://mlsanity.com/paper/2504.03478/) (2025)
- [A Survey of Dataset Refinement for Problems in Computer Vision Datasets](https://mlsanity.com/paper/2210.11717/) (2022)
- [Deep Active Learning in the Presence of Label Noise: A Survey](https://mlsanity.com/paper/2302.11075/) (2023)
- [Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection](https://mlsanity.com/paper/2403.04073/) (2024)
- [Differences Between Hard and Noisy-labeled Samples: An Empirical Study](https://mlsanity.com/paper/2307.10718/) (2023)
- [Improving Label Error Detection and Elimination with Uncertainty Quantification](https://mlsanity.com/paper/2405.09602/) (2024)
- [A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels](https://mlsanity.com/paper/2208.11176/) (2022)
- [Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2210.10426/) (2022)
- [Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook](https://mlsanity.com/paper/2512.09315/) (2025)
- [AI Total: Analyzing Security ML Models with Imperfect Data in Production](https://mlsanity.com/paper/2110.07028/) (2021)
- [How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs](https://mlsanity.com/paper/2205.10504/) (2022)
- [Unsupervised Frequent Pattern Mining for CEP](https://mlsanity.com/paper/2207.14017/) (2022)
- [Active Learning Framework to Automate NetworkTraffic Classification](https://mlsanity.com/paper/2211.08399/) (2022)
- [Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise](https://mlsanity.com/paper/2505.04375/) (2025)
- [Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels](https://mlsanity.com/paper/2509.02351/) (2025)
- [A deep multiple instance learning approach based on coarse labels for high-resolution land-cover mapping](https://mlsanity.com/paper/2510.06769/) (2025)
- [The Gold in Bias: Maturing the AI Design Process through Verification](https://mlsanity.com/paper/2609.29730/) (2026)

## Abstract (arXiv)

Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled by non-specialist annotators, or even specialists in a challenging task, such as in the medical field. Although deep learning models have shown significant improvements in different domains, an open issue is their ability to memorize noisy labels during training, reducing their generalization potential. As deep learning models depend on correctly labeled data sets and label correctness is difficult to guarantee, it is crucial to consider the presence of noisy labels for deep learning training. Several approaches have been proposed in the literature to improve the training of deep learning models in the presence of noisy labels. This paper presents a survey on the main techniques in literature, in which we classify the algorithm in the following groups: robust losses, sample weighting, sample selection, meta-learning, and combined approaches. We also present the commonly used experimental setup, data sets, and results of the state-of-the-art models.