Advances in Semi-Supervised Learning Techniques for Real-World Applications

Abstract

Semi-supervised learning (SSL) has emerged as a powerful technique that leverages both labeled and unlabeled data to improve model performance. This approach is especially crucial in real-world applications where acquiring labeled data is expensive or time-consuming. Recent advances in SSL have led to the development of more efficient algorithms and frameworks, enabling its application in diverse fields such as healthcare, finance, and autonomous systems. This paper explores the latest advancements in SSL, discusses its challenges, and provides insights into its practical applications, highlighting the potential for SSL to revolutionize industries reliant on large amounts of data with limited labeling.