Labeled Data

Labeled data, crucial for training machine learning models, is often scarce and expensive to acquire, driving research into methods that reduce this reliance. Current efforts focus on semi-supervised and self-supervised learning techniques, including approaches like co-training, cross-modality clustering, and adversarial propagation of labels, to leverage unlabeled data and improve model performance with limited labeled examples. These advancements are significant because they enable the development of accurate models in domains where labeled data is limited, impacting fields like medical image analysis, natural language processing, and industrial quality control. The development of novel model architectures, such as capsule networks and ensemble methods, further enhances the efficiency and effectiveness of these data-efficient learning strategies.