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).

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    $\begingroup$ The noise they are refering to is related to the incertainty of the labels. From my understanding they are basically saying that you can use some "dumb" approach to label some extra data (some samples will be correctly labeled some won't) and if you train on it you can expect to get a better model that will outperform the "dumb" approach you use. $\endgroup$
    – mprouveur
    Commented Oct 7, 2020 at 16:14

1 Answer 1


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.


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