Image classification systems recently made a big leap with the advancement of deep neural networks. However, these systems require excessive amount of labeled data in order to be trained properly. This is not always feasible due to several factors, such as expensiveness of labeling process or difficulty of correctly classifying data even for the experts. Because of these practical challenges, label noise is a common problem in datasets and numerous methods to train deep networks with label noise are proposed in literature. Deep networks are known to be relatively robust to label noise, however their tendency to overfit data makes them vulnerable to memorizing even total random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its negative effects for training deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, literature lacks a comprehensive survey of methodologies specifically centered around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them according to their similarity in proposed methodology.
The potential success of one-shot semi-supervised learning relies on the observation that neural networks are relatively robust to label noise REF .
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
ArXiv
ArXiv
Mathematics Computer Science