  In many machine learning applications, labeling datasets can be an arduous
and time-consuming task. Although research has shown that semi-supervised
learning techniques can achieve high accuracy with very few labels within the
field of computer vision, little attention has been given to how images within
a dataset should be selected for labeling. In this paper, we propose a novel
approach based on well-established self-supervised learning, clustering, and
manifold learning techniques that address this challenge of selecting an
informative image subset to label in the first instance, which is known as the
cold-start or unsupervised selective labelling problem. We test our approach
using several publicly available datasets, namely CIFAR10, Imagenette,
DeepWeeds, and EuroSAT, and observe improved performance with both supervised
and semi-supervised learning strategies when our label selection strategy is
used, in comparison to random sampling. We also obtain superior performance for
the datasets considered with a much simpler approach compared to other methods
in the literature.
