Semi Supervised Classification

Semi-supervised classification aims to improve machine learning model accuracy by leveraging both labeled and unlabeled data, addressing the scarcity of labeled data often encountered in real-world applications. Current research focuses on developing novel algorithms and model architectures, including graph neural networks, variational autoencoders, and transformer-based models, to effectively integrate unlabeled data and improve classification performance, often employing techniques like pseudo-labeling and consistency regularization. These advancements are particularly impactful in domains like medical image analysis and remote sensing where labeled data is expensive or difficult to obtain, enabling more efficient and accurate analysis with limited resources. The resulting improvements in model accuracy and efficiency have broad implications across various scientific fields and practical applications.