What is noise-tolerant learning? I was reading this paper and came across the below paragraph. Can you please help me understand what does the highlighted term noise-tolerant learning or noisy-labeled training data mean with a simple example and how is it useful when we don't have labels in our dataset etc? I am learning ML and your inputs would be helpful

To address the scarcity of labeled training data, Chen et al used
active learning to intelligently select training samples for labeling,
demonstrating that classifier performance could be preserved with
fewer samples.16 Another trend is the use of “silver standard training
sets,” a semisupervised approach where training samples are labeled
using an imperfect heuristic rather than by manual review.17–22 The
intuition is that noise-tolerant classifiers trained on imperfectly
labeled data will abstract higher order properties of the phenotype
beyond the original labeling heuristic (so-called “noise-tolerant
learning”23).
Learning that can generalise well.
Take for example differnetial privacy. There you inject noise on Purpose to anonymise your data, and in the process of you losse accuracy. Goal is to find such algorithms, that will with smart noise injections, be able to generalise and Keep the good accuracy Level.