  In active learning, the focus is mainly on the selection strategy of
unlabeled data for enhancing the generalization capability of the next learning
cycle. For this, various uncertainty measurement methods have been proposed. On
the other hand, with the advent of data augmentation metrics as the regularizer
on general deep learning, we notice that there can be a mutual influence
between the method of unlabeled data selection and the data augmentation-based
regularization techniques in active learning scenarios. Through various
experiments, we confirmed that consistency-based regularization from analytical
learning theory could affect the generalization capability of the classifier in
combination with the existing uncertainty measurement method. By this fact, we
propose a methodology to improve generalization ability, by applying data
augmentation-based techniques to an active learning scenario. For the data
augmentation-based regularization loss, we redefined cutout (co) and cutmix
(cm) strategies as quantitative metrics and applied at both model training and
unlabeled data selection steps. We have shown that the augmentation-based
regularizer can lead to improved performance on the training step of active
learning, while that same approach can be effectively combined with the
uncertainty measurement metrics proposed so far. We used datasets such as
FashionMNIST, CIFAR10, CIFAR100, and STL10 to verify the performance of the
proposed active learning technique for multiple image classification tasks. Our
experiments show consistent performance gains for each dataset and budget
scenario.
