Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

The recent success of deep learning is mostly due to the availability of big datasets with
clean annotations. However, gathering a cleanly annotated dataset is not always feasible …

Deep neural networks are known to be data-driven and label noise can have a marked
impact on model performance. Recent studies have shown great robustness to classic …

Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

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 …

Label noise has been a practical challenge in deep learning due to the strong capability of
deep neural networks in fitting all training data. Prior literature primarily resorts to sample …

Imperfections in data annotation, known as label noise, are detrimental to the training of
machine learning models and have a confounding effect on the assessment of model …

Deep neural networks trained on large supervised datasets have led to impressive results in
image classification and other tasks. However, well-annotated datasets can be time …

Label noise is frequently observed in real-world large-scale datasets. The noise is
introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most …