Efficient Semi Supervised

Efficient semi-supervised learning aims to leverage both labeled and unlabeled data to train accurate machine learning models, mitigating the limitations of data scarcity. Current research focuses on developing computationally efficient algorithms, such as incremental self-training and methods incorporating low-rank adaptations of pre-trained models, often within the context of specific applications like medical image analysis and subspace clustering. These advancements improve model performance and reduce computational demands, making semi-supervised learning more practical for diverse fields requiring large datasets but limited labeled examples. The resulting models offer improved accuracy and efficiency, impacting various applications from disease detection to image segmentation.