In machine learning, it is often the case that training labels are subject to noise such as mislabelling. For neural networks that require large quantities of training data, this manifests as a trade-off between dataset quality and quantity. For instance, a model may have good performance on a training set (with noisy labels), but when we evaluate on a manually annotated test set, the model appears to generalize poorly.

What are some ways a machine learning practitioner can better deal with this problem?