# Deep Learning Classification With Noisy Labels

arXiv: https://arxiv.org/abs/2004.11116 · HTML: https://mlsanity.com/paper/2004.11116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [How Does Heterogeneous Label Noise Impact Generalization in Neural Nets?](https://mlsanity.com/paper/2106.15475/) (2021)

## Abstract (arXiv)

Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the training set. Indexing multimedia content for retrieval, classification or recommendation can involve tagging or classification based on multiple criteria. In our case, we train face recognition systems for actors identification with a closed set of identities while being exposed to a significant number of perturbators (actors unknown to our database). Face classifiers are known to be sensitive to label noise. We review recent works on how to manage noisy annotations when training deep learning classifiers, independently from our interest in face recognition.