This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label.
Notations    Symbol Meaning     $K$ Number of unique class labels.   $(\mathbf{x}^l, y) \sim \mathcal{X}, y \in \{0, 1\}^K$ Labeled dataset.

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This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label.

Learning with not Enough Data Part 2: Active Learning