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.

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

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A Survey of Deep Active Learning

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.

Stream-based Selective Sampling 

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.

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