Unlabeled Learning

Unlabeled learning encompasses various techniques aiming to train machine learning models using datasets where a significant portion of the data lacks explicit labels. Current research focuses on improving the accuracy and robustness of models trained with positive and unlabeled data (PU learning) and semi-supervised learning (SSL), often employing methods like consistency regularization, pseudo-labeling, and novel loss functions to leverage unlabeled information effectively. These advancements are crucial for addressing data scarcity in many applications, such as medical diagnosis, anomaly detection, and natural language processing, where obtaining labeled data is expensive or impractical. The development of more efficient and reliable unlabeled learning methods promises to significantly impact various fields by enabling the use of larger, readily available datasets.