weak supervision

Labels are the costly bottleneck in ML, and these two techniques tackle it from different angles. What matters is knowing active learning picks what to label while semi-supervised draws on unlabeled data directly. Here is the answer.

Hand-labeling at scale is the bottleneck. Weak supervision produces labels programmatically instead, and what matters is whether you can explain how a label model denoises conflicting sources into probabilistic labels. Here is the answer.

Plenty of real problems hand you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone grabs quietly biases the model. What shows depth is naming the regime and its fix. Here is the answer.

A small labeled set is the normal place to start, not an excuse. The strong answer is a ladder of techniques that pull signal from unlabeled data, pretrained models, and the labeling budget. Here it is.