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.

University of Michigan - Ann Arbor

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

Deep Bayesian Active Learning with Image Data

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.

Batch Mode DeepAL

Deep Bayesian Active Learning

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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