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.

提出了一种名为 ALFA-Mix 的新型批量主动学习方法，它利用分析未标记实例的预测结果中的不一致性来确定具有足够明显特征的实例，证明该方法在图像、视频和非视觉数据的 12 个基准测试中，在 30 种不同的设置中优于所有其它主动学习方法，特别是在低数据量情况和自训练视觉转换器方面，ALFA-Mix 的性能优于该领域现有技术的 59％和 43％。