We propose a model-agnostic UMPL framework that leverages teacher-student uncertainty to refine pseudo labels and improve generalization with unlabeled data.

We propose an uncertainty-aware pseudo-labeling framework that dynamically adjusts pseudo-label influence from a bi-level optimization perspective.