Active Labeling

Active labeling is based on selecting the most informative examples for annotation instead of labeling all data equally. Typically, uncertain, boundary-case, or diversity-critical samples are prioritized. This can produce greater model improvement with less annotation cost. It is especially valuable in domains where expert labeling is expensive. Active labeling is not about collecting more labels, but about collecting labels more intelligently.