Continuous Pseudo Labeling

Continuous pseudo-labeling (CPL) is a semi-supervised learning technique that leverages unlabeled data by assigning continuous confidence scores, rather than hard labels, to predictions made on this data. Current research focuses on applying CPL across diverse domains, including medical image segmentation, speech recognition (both audio-visual and audio-only), and vision-language tasks, often incorporating it within frameworks like masked context modeling or generative models to improve robustness and mitigate biases. The effectiveness of CPL in improving model performance with limited labeled data makes it a significant advancement for various applications where obtaining large labeled datasets is challenging or expensive.