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 $$L^m$$.

University of Michigan - Ann Arbor

Google

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

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

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.

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