Separate sampling based on uncertainty often results in sampling bias, the currently selected sample is not representative of the distribution of unlabeled datasets.

The Disadvantage of Uncertainty-based Strategy

Considering only strategies that promote diversity in sampling may lead to increased labeling costs, as maybe a considerable number of samples with low information content will consequently be selected.

The Disadvantage of Diversity-based Strategy.

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

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

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

Active Learning Categories

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

We define $U^n = {X,Y}$ as an unlabeled dataset with n labels. 
$X$ 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 $L^m$.


Query Strategy Formalization of Deep 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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