  Active learning effectively collects data instances for training deep
learning models when the labeled dataset is limited and the annotation cost is
high. Besides active learning, data augmentation is also an effective technique
to enlarge the limited amount of labeled instances. However, the potential gain
from virtual instances generated by data augmentation has not been considered
in the acquisition process of active learning yet. Looking ahead the effect of
data augmentation in the process of acquisition would select and generate the
data instances that are informative for training the model. Hence, this paper
proposes Look-Ahead Data Acquisition via augmentation, or LADA, to integrate
data acquisition and data augmentation. LADA considers both 1) unlabeled data
instance to be selected and 2) virtual data instance to be generated by data
augmentation, in advance of the acquisition process. Moreover, to enhance the
informativeness of the virtual data instances, LADA optimizes the data
augmentation policy to maximize the predictive acquisition score, resulting in
the proposal of InfoMixup and InfoSTN. As LADA is a generalizable framework, we
experiment with the various combinations of acquisition and augmentation
methods. The performance of LADA shows a significant improvement over the
recent augmentation and acquisition baselines which were independently applied
to the benchmark datasets.
