# Deep Learning Classification With Noisy Labels

Source: [Deep Learning Classification With Noisy Labels](<https://www.sota2.com/research/paper/68fa74cb1e209131cea63e4c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:18:28.201Z

Authors: Guillaume Sanchez, Vincente Guis, Ricard Marxer, Fr\\'ed\\'eric Bouchara

Published: 2020-04-23T13:02:45.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.11116>)

[PDF](<https://arxiv.org/pdf/2004.11116.pdf>)