{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cold-paws-unsupervised-class-discovery-and-1","title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","arxiv_id":"2305.10071","date":"2023-05-17","proceeding":null,"authors":["Evelyn J. Mannix","Howard D. Bondell"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2305.10071v2","url_pdf":"https://arxiv.org/pdf/2305.10071v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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