Sparse Label

Sparse label learning addresses the challenge of training machine learning models with limited labeled data, aiming to improve model performance and reduce the high cost of extensive data annotation. Current research focuses on developing novel algorithms and model architectures, such as self-training methods, graph neural networks, and the incorporation of foundation models like Segment Anything Model (SAM), to effectively leverage both labeled and unlabeled data. This field is significant because it enables the application of machine learning to diverse domains with limited labeled data, including medical imaging, remote sensing, and natural language processing, leading to more efficient and cost-effective model development.