Jiaxuan Jiang

Semi-Supervised Regression by Preserving Ranking Relationships Between Close Unlabeled Samples

Semi-Supervised Learning (SSL) aims to improve the learning performance of supervised learning with a large number of unlabeled samples. The existing SSL methods such as FixMatch and FlexMatch select unlabeled samples with high-confident pseudo-labels and make consistency constraints between their w