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.

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.

Pool-based Sampling

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

University of Michigan - Ann Arbor

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible.

Active Learning

A Survey of Deep Active Learning

Active Learning Categories

1. Uncertainty-based Strategy.
2. Diversity-based Strategy.
3. Expected Model Change.

Query Strategy Categories of Active Learning

1. Model uncertainty in Deep Learning. 
2. Insufficient data for labeled samples.
3. Processing pipeline inconsistency.

Challenges of Combining Deep Learning and Active Learning

1. Batch Mode DeepAL.
2. Uncertainty-based and Hybrid Query Strategies.
3. Deep Bayesian Active Learning. (DBAL)
4. Density-based Methods.
5. Automated Design of DeepAL.

Key Aspects of Query Strategy Design

Deep AL's Research Examples

We define $$U^n = \{X,Y\}$$ as an unlabeled dataset with $$n$$ samples. $$X$$ is the sample space. $$Y$$ is the label space. $$P(x,y)$$ is a potential distribution. $$L^m = \{X,Y\}$$ is the current labeled training set with $$m$$ samples. Query strategy is $$Q$$ which maps $$U^n$$ to $$L^m$$.

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