Abstract
2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images
(SIBGRAPI) 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.