  The remarkable performance of deep neural networks depends on the
availability of massive labeled data. To alleviate the load of data annotation,
active deep learning aims to select a minimal set of training points to be
labelled which yields maximal model accuracy. Most existing approaches
implement either an `exploration'-type selection criterion, which aims at
exploring the joint distribution of data and labels, or a `refinement'-type
criterion which aims at localizing the detected decision boundaries. We propose
a versatile and efficient criterion that automatically switches from
exploration to refinement when the distribution has been sufficiently mapped.
Our criterion relies on a process of diffusing the existing label information
over a graph constructed from the hidden representation of the data set as
provided by the neural network. This graph representation captures the
intrinsic geometry of the approximated labeling function. The diffusion-based
criterion is shown to be advantageous as it outperforms existing criteria for
deep active learning.
