Uncertainty Aware Pseudo Label

Uncertainty-aware pseudo-labeling improves semi-supervised learning by carefully selecting pseudo-labels for unlabeled data based on model confidence estimates. Current research focuses on developing robust uncertainty quantification methods, often employing techniques like conformal prediction or ensemble methods, to filter out unreliable pseudo-labels and mitigate the negative impact of noisy data. This approach enhances the performance of various machine learning tasks, including image recognition, stereo matching, and text recognition, by effectively leveraging unlabeled data while avoiding overfitting to erroneous predictions. The resulting improvements in model accuracy and robustness have significant implications for applications where labeled data is scarce or expensive.