BABA: Beta Approximation for Bayesian Active Learning

This paper introduces a new acquisition function under the Bayesian active learning framework, namely BABA. It is motivated by previously well-established works BALD, and BatchBALD which capture the mutual information between the model parameters and the predictive outputs of the data. Our proposed measure, BABA, endeavors to quantify the normalized mutual information by approximating the stochasticity of predictive probabilities using Beta distributions. BABA outperforms the well-known family of acquisition functions, including BALD and BatchBALD. We demonstrate this by showing extensive experimental results obtained from MNIST and EMNIST datasets.