  While annotating decent amounts of data to satisfy sophisticated learning
models can be cost-prohibitive for many real-world applications. Active
learning (AL) and semi-supervised learning (SSL) are two effective, but often
isolated, means to alleviate the data-hungry problem. Some recent studies
explored the potential of combining AL and SSL to better probe the unlabeled
data. However, almost all these contemporary SSL-AL works use a simple
combination strategy, ignoring SSL and AL's inherent relation. Further, other
methods suffer from high computational costs when dealing with large-scale,
high-dimensional datasets. Motivated by the industry practice of labeling data,
we propose an innovative Inconsistency-based virtual aDvErsarial Active
Learning (IDEAL) algorithm to further investigate SSL-AL's potential
superiority and achieve mutual enhancement of AL and SSL, i.e., SSL propagates
label information to unlabeled samples and provides smoothed embeddings for AL,
while AL excludes samples with inconsistent predictions and considerable
uncertainty for SSL. We estimate unlabeled samples' inconsistency by
augmentation strategies of different granularities, including fine-grained
continuous perturbation exploration and coarse-grained data transformations.
Extensive experiments, in both text and image domains, validate the
effectiveness of the proposed algorithm, comparing it against state-of-the-art
baselines. Two real-world case studies visualize the practical industrial value
of applying and deploying the proposed data sampling algorithm.
