  Self-supervised contrastive learning offers a means of learning informative
features from a pool of unlabeled data. In this paper, we delve into another
useful approach -- providing a way of selecting a core-set that is entirely
unlabeled. In this regard, contrastive learning, one of a large number of
self-supervised methods, was recently proposed and has consistently delivered
the highest performance. This prompted us to choose two leading methods for
contrastive learning: the simple framework for contrastive learning of visual
representations (SimCLR) and the momentum contrastive (MoCo) learning
framework. We calculated the cosine similarities for each example of an epoch
for the entire duration of the contrastive learning process and subsequently
accumulated the cosine-similarity values to obtain the coreset score. Our
assumption was that an sample with low similarity would likely behave as a
coreset. Compared with existing coreset selection methods with labels, our
approach reduced the cost associated with human annotation. The unsupervised
method implemented in this study for coreset selection obtained improved
results over a randomly chosen subset, and were comparable to existing
supervised coreset selection on various classification datasets (e.g., CIFAR,
SVHN, and QMNIST).
