Semi Supervision

Semi-supervised learning aims to improve machine learning model performance by leveraging both labeled and unlabeled data, addressing the scarcity of labeled data which is a common bottleneck. Current research focuses on developing techniques that effectively utilize unlabeled data, including methods employing large language models for pseudo-labeling, adversarial training to improve data selection, and self-supervised learning to create auxiliary tasks. These advancements are significantly impacting various fields, such as medical image analysis and natural language processing, by enabling the development of high-performing models with reduced annotation costs and improved efficiency.