The cycle ends when the label budget is exhausted or the pre-defined termination conditions are reached.

Pool-based Active Learning Framework

In pool-based sampling, the algorithm selects samples based on “informativeness". Informativeness is quantified based on a user-selected metric, which users choose based on the requirements of their application.

University of Michigan - Ann Arbor

AL approaches can be divided into membership query synthesis, stream-based selective sampling, and pool-based AL from application scenarios.

Active Learning Categories

A Survey of Deep Active Learning

Membership query synthesis means that the learner can request to query the label of any unlabeled sample in the input space, including the sample generated by the learner.

Membership Query Synthesis

It is a sequential strategy where we can randomly sample data points from a sampled distribution and then select if we want to label it or not.

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