Pseudo Label Quality

Pseudo label quality is a critical factor in semi-supervised and weakly-supervised learning, where models learn from a combination of labeled and unlabeled data. Research focuses on improving the accuracy and reliability of pseudo labels generated for unlabeled data through techniques like contrastive learning, meta-learning, and confidence-based filtering, often incorporating data augmentation strategies. These advancements aim to enhance the performance of models trained with limited labeled data, impacting various fields including medical image segmentation, temporal action localization, and speech processing. Improved pseudo label quality directly translates to better model accuracy and efficiency, reducing the reliance on expensive and time-consuming data annotation.