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

Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning