Researchers have considered modifying the combined framework of AL and DL to make the proposed DeepAL model as general as possible, an approach that can be extended to various application fields. This is of great significance to the promotion of DeepAL.

Methods to Tackle Pipeline Inconsistency.

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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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

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.

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.

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