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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/43BHB8L
Repositorysid.inpe.br/sibgrapi/2020/09.30.23.54
Last Update2020:09.30.23.54.49 filipe.rolim@ufrpe.br
Metadatasid.inpe.br/sibgrapi/2020/09.30.23.54.49
Metadata Last Update2020:10.28.20.46.59 administrator
Citation KeyCordeiroCarn:2020:HoTrYo
TitleA Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?
FormatOn-line
Year2020
DateNov. 7-10, 2020
Access Date2020, Dec. 04
Number of Files1
Size494 KiB
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Author1 Cordeiro, Filipe Rolim
2 Carneiro, Gustavo
Affiliation1 Universidade Federal Rural de Pernambuco
2 University of Adelaide
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressfilipe.rolim@ufrpe.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2020-09-30 23:54:49 :: filipe.rolim@ufrpe.br -> administrator ::
2020-10-28 20:46:59 :: administrator -> filipe.rolim@ufrpe.br :: 2020
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Document Stagecompleted
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Transferable1
Content TypeExternal Contribution
Tertiary TypeTutorial
Keywordsnoisy labels, deep learning.
AbstractNoisy 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.
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Languageen
Target FileTutorial_ID_4_SIBGRAPI_2020_camara_ready_v2 copy.pdf
e-Mail (login)filipe.rolim@ufrpe.br
User Groupfilipe.rolim@ufrpe.br
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Next Higher Units8JMKD3MGPEW34M/43G4L9S
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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