Pseudo Label Regularization

Pseudo-label regularization is a technique used to improve the performance of machine learning models by leveraging unlabeled data. Current research focuses on refining pseudo-label generation, often employing techniques like adversarial attacks or co-regularization to enhance their quality and reliability across diverse model architectures, including those based on contrastive learning and prototypical networks. This approach is particularly valuable in scenarios with limited labeled data, imbalanced datasets, or domain shifts, leading to improved performance in various applications such as object detection, 3D reconstruction, and few-shot learning. The resulting improvements in model robustness and accuracy have significant implications for various fields.