If you're training models with scarce labels and a tight annotation budget, this walks through active learning methods for choosing which samples are worth labeling — a practical lever for maximizing model quality per dollar of human labeling.
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.
Transcript
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.
