It leads to frequent training of the learning model but little change in the training data. The training set obtained by this query method is not only inefficient in the training of the DL model, but can also easily lead to overfitting. 

Disadvantage of One-by-one Query Method

At each acquisition step, we score a batch of candidate unlabeled data samples based on the acquisition function used and the deep model trained on the labelled set.

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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

A Survey of Deep Active Learning

Batch Mode DeepAL

It combines BCNNs (Bayesian Convolutional Neural Networks) with AL methods
to adapt BALD to the deep learning environment, thereby developing a new AL framework for high-dimensional data.

Deep Bayesian Active Learning

Deep Bayesian Active Learning with Image Data

Bayesian Active Learning for Classification and Preference Learning

DBAL is unsuitable for large datasets due to the need for batch sampling.  DBAL allows the use of dropout in testing for better confidence estimation. the performance of this method is similar to the performance of using neural network SR as uncertainty sampling, which requires vigilance.

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