@InProceedings{CordeiroCarn:2020:HoTrYo,
author = "Cordeiro, Filipe Rolim and Carneiro, Gustavo",
affiliation = "{Universidade Federal Rural de Pernambuco} and {University of
Adelaide}",
title = "A Survey on Deep Learning with Noisy Labels: How to train your
model when you cannot trust on the annotations?",
booktitle = "Proceedings...",
year = "2020",
editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and
Pelechano, Nuria and Wang, Zhangyang (Atlas)",
organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "noisy labels, deep learning.",
abstract = "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.",
conference-location = "Porto de Galinhas (virtual)",
conference-year = "7-10 Nov. 2020",
doi = "10.1109/SIBGRAPI51738.2020.00010",
url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00010",
language = "en",
ibi = "8JMKD3MGPEW34M/43BHB8L",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BHB8L",
targetfile = "Tutorial_ID_4_SIBGRAPI_2020_camara_ready_v2 copy.pdf",
urlaccessdate = "2025, Feb. 16"
}