Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
This addresses the challenge of efficient label selection for practitioners in computer vision, though it is incremental as it builds on existing self-supervised and clustering techniques.
The paper tackles the cold-start problem in semi-supervised learning by proposing a method to select informative images for labeling, resulting in improved performance over random sampling on datasets like CIFAR10 and Imagenette.
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.