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

• URL: http://arxiv.org/abs/2305.10071v2
• Date: Tue, 6 Jun 2023 07:31:15 GMT
• Title: Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
• Authors: Evelyn J. Mannix, Howard D. Bondell
• Abstract summary: We propose a novel approach based on well-established self-supervised learning, clustering, and manifold learning techniques. We test our approach using several publicly available datasets, namely CIFAR10, Imagenette, DeepWeeds, and EuroSAT. We obtain superior performance for the datasets considered with a much simpler approach compared to other methods in the literature.
• Score: 0.30458514384586394
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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