Learning with not Enough Data Part 1: Semi-Supervised Learning
<!-- The performance of supervised learning tasks improves with more high-quality labels available. However, it is expensive to collect a large number of labeled samples. There are several paradigms in machine learning to deal with the scenario when the labels are scarce. Semi-supervised learning is one candidate, utilizing a large amount of unlabeled data conjunction with a small amount of labeled data. --> <p>When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.</p>