  The promise of active learning (AL) is to reduce labelling costs by selecting
the most valuable examples to annotate from a pool of unlabelled data.
Identifying these examples is especially challenging with high-dimensional data
(e.g. images, videos) and in low-data regimes. In this paper, we propose a
novel method for batch AL called ALFA-Mix. We identify unlabelled instances
with sufficiently-distinct features by seeking inconsistencies in predictions
resulting from interventions on their representations. We construct
interpolations between representations of labelled and unlabelled instances
then examine the predicted labels. We show that inconsistencies in these
predictions help discovering features that the model is unable to recognise in
the unlabelled instances. We derive an efficient implementation based on a
closed-form solution to the optimal interpolation causing changes in
predictions. Our method outperforms all recent AL approaches in 30 different
settings on 12 benchmarks of images, videos, and non-visual data. The
improvements are especially significant in low-data regimes and on self-trained
vision transformers, where ALFA-Mix outperforms the state-of-the-art in 59% and
43% of the experiments respectively.
