A clear walkthrough of the main approaches for training when labeled data is scarce, giving practitioners concrete semi-supervised learning strategies to squeeze more value from unlabeled datasets.
When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
Transcript
When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
