Supervised learning models use labeled data to learn and infer patterns, which they can then apply to real-world unlabeled information. Some examples of the utility of labeled data include ...

In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output. Unsupervised learning, on the other hand, deals with unlabeled data ...

However, as the coauthors of the paper note, labeled data is generally harder to come by than unlabeled data ... Fine-tuning took place on between eight and 24 graphics cards.