Some researchers have applied Bayesian deep learning to deal with the high-dimensional mini-batch samples with fewer queries in the AL context, , thereby effectively alleviating the problem of the DL model being too confident about the output results.

Methods to Tackle Model Uncertainty in Deep Learning for Active Learning

The SR(Softmax Response) layer's output can be too confident so that it is unreliable as a measure of confidence and the performance of this method will thus be even worse than that of random sampling.

University of Michigan - Ann Arbor

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

Challenges of Combining Deep Learning and Active Learning

A Survey of Deep Active Learning

Model Uncertainty in Deep Learning for Active Learning

AL often relies on a small amount of labeled sample data to learn and update the model, while DL is often very greedy for data. Data for every iteration may not be enough to train a good model.

Insufficient Data for Labeled Samples.

The processing pipelines of AL and DL are inconsistent. Most AL algorithms focus primarily on the training of classifiers and the various query strategies utilized are largely based on fixed feature representations.

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