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Reference TypeConference Paper (Conference Proceedings)
Last Update2020:
Metadata Last Update2020: 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?
Access Date2021, June 18
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)
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationVirtual
DateNov. 7-10, 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeTutorial
History2020-09-30 23:54:49 :: -> administrator ::
2020-10-28 20:46:59 :: administrator -> :: 2020
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Is the master or a copy?is the master
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Content TypeExternal Contribution
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. > SDLA > SIBGRAPI 2020 > A Survey on...
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