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 …

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

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …

Real-world data is never perfect and can often suffer from corruptions (noise) that may
impact interpretations of the data, models created from the data and decisions made based …

The occurrences of noisy data in data set can significantly impact prediction of any
meaningful information. Many empirical studies have shown that noise in data set …

This paper presents a new approach for identifying and eliminating mislabeled instances in
large or distributed datasets. We first partition a dataset into subsets, each of which is small …

Ensemble methods combine a set of classifiers to construct a new classifier that is (often)
more accurate than any of its component classifiers. In this paper, we use ensemble …

The existing noise detection methods required the classifiers or distance measurements or
data overall distribution, andcurse of dimensionality'and other restrictions made them …

The task of continual learning requires careful design of algorithms that can tackle
catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario …