Fully Supervised

Fully supervised learning, while effective, suffers from the high cost and time associated with extensive data annotation. Current research focuses on reducing this reliance through semi-supervised and self-supervised techniques, leveraging unlabeled data alongside labeled data or employing auxiliary tasks to improve model robustness and generalization. These approaches, often incorporating contrastive learning, teacher-student models, and various data augmentation strategies, aim to achieve performance comparable to fully supervised methods with significantly less labeled data. This research is crucial for advancing applications in various fields, including medical image analysis, computer vision, and natural language processing, where labeled data is scarce or expensive to obtain.