  The success of deep active learning hinges on the choice of an effective
acquisition function, which ranks not yet labeled data points according to
their expected informativeness. Many acquisition functions are (partly) based
on the uncertainty that the current model has about the class label of a point,
yet there is no generally agreed upon strategy for computing such uncertainty.
This paper proposes a new and very simple approach to computing uncertainty in
deep active learning with a Convolutional Neural Network (CNN). The main idea
is to use the feature representation extracted by the CNN as data for training
a Sum-Product Network (SPN). Since SPNs are typically used for estimating the
distribution of a dataset, they are well suited to the task of estimating class
probabilities that can be used directly by standard acquisition functions such
as max entropy and variational ratio. The effectiveness of our method is
demonstrated in an experimental study on several standard benchmark datasets
for image classification, where we compare it to various state-of-the-art
methods for assessing uncertainty in deep active learning.
