Limited Label

Limited label learning tackles the challenge of training effective machine learning models with minimal labeled data, a common constraint in many real-world applications. Current research focuses on leveraging unlabeled data through techniques like self-supervised learning, pseudo-labeling (including smoothed versions), and transfer learning, often employing deep learning architectures such as StyleGANs, Graph Neural Networks, and various Large Language Models. These methods aim to improve model performance in scenarios with scarce labeled examples, impacting diverse fields from medical image analysis and social media monitoring to agricultural applications and fraud detection. The ultimate goal is to enable accurate and efficient model training even when obtaining labeled data is expensive or difficult.