Novel Semi Supervised

Novel semi-supervised learning methods aim to improve machine learning model performance by effectively utilizing both labeled and unlabeled data, addressing the limitations of fully supervised approaches that require extensive, often expensive, annotation. Current research focuses on developing robust algorithms, such as teacher-student architectures, contrastive learning, and self-training methods, often incorporating uncertainty estimation to improve pseudo-label generation and selection. These advancements are significantly impacting various fields, including medical image analysis, object detection, and action recognition, by enabling the training of high-performing models with limited labeled data.