Unlabeled Dataset

Unlabeled datasets are a crucial resource in machine learning, offering the potential to improve model performance and reduce the need for expensive data labeling. Current research focuses on effectively integrating unlabeled data into various learning paradigms, including semi-supervised learning, positive-unlabeled learning, and open-set learning, often employing techniques like pseudo-labeling, consistency regularization, and adversarial training within diverse model architectures. This work is significant because it addresses the limitations of labeled data scarcity in many real-world applications, leading to more robust and efficient machine learning models across diverse fields like manufacturing, medical imaging, and remote sensing.