  The availability of large labeled datasets is the key component for the
success of deep learning. However, annotating labels on large datasets is
generally time-consuming and expensive. Active learning is a research area that
addresses the issues of expensive labeling by selecting the most important
samples for labeling. Diversity-based sampling algorithms are known as integral
components of representation-based approaches for active learning. In this
paper, we introduce a new diversity-based initial dataset selection algorithm
to select the most informative set of samples for initial labeling in the
active learning setting. Self-supervised representation learning is used to
consider the diversity of samples in the initial dataset selection algorithm.
Also, we propose a novel active learning query strategy, which uses
diversity-based sampling on consistency-based embeddings. By considering the
consistency information with the diversity in the consistency-based embedding
scheme, the proposed method could select more informative samples for labeling
in the semi-supervised learning setting. Comparative experiments show that the
proposed method achieves compelling results on CIFAR-10 and Caltech-101
datasets compared with previous active learning approaches by utilizing the
diversity of unlabeled data.
