Using semi-supervised learning methods, such as incorporating unsupervised clustering on unlabeled data to assist and refine the labeled data, has proven effective in handling label noise.

I have found that leveraging crowd-sourcing platforms with careful quality control measures can help manage label noise by distributing the annotation task among a large number of contributors.

In my experience, establishing clear annotation guidelines and conducting regular training sessions with annotators has been crucial in minimizing label noise and ensuring consistency.

I have had success with active learning techniques, where the model iteratively selects the most uncertain data points for additional annotation, helping to refine the labeling process.

Applying techniques like active learning combined with human-in-the-loop review processes have been successful in identifying and correcting label noise in the datasets I have worked with.

I have found that conducting thorough quality checks on a subset of labeled data and providing feedback to annotators helps to improve labeling accuracy and reduce noise.

One strategy I have found effective is using a consensus-based approach, where multiple annotators label the same data point and the final label is determined by majority voting.