  Active Learning (AL) and Semi-supervised Learning are two techniques that
have been studied to reduce the high cost of deep learning by using a small
amount of labeled data and a large amount of unlabeled data. To improve the
accuracy of models at a lower cost, we propose a method called Active
Semi-supervised Learning (ASSL), which combines AL and SSL. To maximize the
synergy between AL and SSL, we focused on the differences between ASSL and AL.
ASSL involves more dynamic model updates than AL due to the use of unlabeled
data in the training process, resulting in the temporal instability of the
predicted probabilities of the unlabeled data. This makes it difficult to
determine the true uncertainty of the unlabeled data in ASSL. To address this,
we adopted techniques such as exponential moving average (EMA) and upper
confidence bound (UCB) used in reinforcement learning. Additionally, we
analyzed the effect of label noise on unsupervised learning by using weak and
strong augmentation pairs to address datainconsistency. By considering both
uncertainty and datainconsistency, we acquired data samples that were used in
the proposed ASSL method. Our experiments showed that ASSL achieved about 5.3
times higher computational efficiency than SSL while achieving the same
performance, and it outperformed the state-of-the-art AL method.
