What exactly is label noise?
One of the main tasks in machine learning is classification. For example, given an image of a digit, which digit it represents? The desired algorithm assigns a label – in this case, a digit, to each sample – in this case, an image.
Supervised machine learning algorithms train classification algorithms using labelled data. The labels in the training set are typically manually generated by humans, who sometimes mislabel data. This is known as label noise. Label noise is usually the result of honest mistakes, but sometimes occurs out of malice. A third possibility is that some samples are genuinely hard to classify. Yet another possibility is that some labels are correct, but for various reasons we do not condone them.
Label noise is an issue since most machine learning models are trained on labelled data, and their performance is closely tied to the quality of the data they are trained on.