Pseudo Label Sampling

Pseudo-label sampling leverages unlabeled data by assigning predicted labels (pseudo-labels) to improve model training, particularly in semi-supervised learning and scenarios with noisy or imbalanced data. Current research focuses on improving the reliability of pseudo-labels through techniques like uncertainty estimation and discriminative sampling, often incorporating self-supervised learning and transformer architectures. These advancements aim to enhance model accuracy and robustness, especially in resource-constrained settings or when dealing with complex data distributions, impacting various applications from image classification and object localization to dialogue systems and semantic segmentation.