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Noisy Labels are commonly present in data sets automatically collected fromthe internet, mislabeled by non-specialist annotators, or even specialists in achallenging task, such as in the medical field. Although deep learning modelshave shown significant improvements in different domains, an open issue istheir ability to memorize noisy labels during training, reducing theirgeneralization potential. As deep learning models depend on correctly labeleddata sets and label correctness is difficult to guarantee, it is crucial toconsider the presence of noisy labels for deep learning training. Severalapproaches have been proposed in the literature to improve the training of deeplearning models in the presence of noisy labels. This paper presents a surveyon the main techniques in literature, in which we classify the algorithm in thefollowing groups: robust losses, sample weighting, sample selection,meta-learning, and combined approaches. We also present the commonly usedexperimental setup, data sets, and results of the state-of-the-art models.

A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?