Certainty-Aware Partition and Sufficient Utilization of Noisy Samples
CAPSUN is proposed, a robust framework that improves the precision of clean sample selection and mitigates distribution bias through alignment among subsets, and designs a distribution alignment module to adjust the class distribution contrast of labeled and unlabeled subsets to mitigate class distribution discrepancies.