Pseudo Label Filtering

Pseudo-label filtering aims to improve the accuracy of semi-supervised and unsupervised learning methods by selectively incorporating predictions from unlabeled data (pseudo-labels) into the training process. Current research focuses on developing sophisticated filtering techniques, often incorporating uncertainty estimation, confidence scores, or contrastive learning to identify and remove noisy pseudo-labels, thereby enhancing model performance. These advancements are particularly impactful in domains with limited labeled data, such as medical image segmentation and speech processing, where they enable more efficient and effective model training and improve the generalization capabilities of models across different data distributions.