RankMatch: A Novel Approach to Semi-Supervised Label Distribution
  Learning Leveraging Inter-label Correlations
Kouzhiqiang Yucheng Xie Jing Wang Yuheng Jia Boyu Shi Xin Geng
This paper introduces RankMatch, an innovative approach for Semi-Supervised
Label Distribution Learning (SSLDL). Addressing the challenge of limited
labeled data, RankMatch effectively utilizes a small number of labeled examples
in conjunction with a larger quantity of unlabeled data, reducing the need for
extensive manual labeling in Deep Neural Network (DNN) applications.
Specifically, RankMatch introduces an ensemble learning-inspired averaging
strategy that creates a pseudo-label distribution from multiple weakly
augmented images. This not only stabilizes predictions but also enhances the
model's robustness. Beyond this, RankMatch integrates a pairwise relevance
ranking (PRR) loss, capturing the complex inter-label correlations and ensuring
that the predicted label distributions align with the ground truth.
  We establish a theoretical generalization bound for RankMatch, and through
extensive experiments, demonstrate its superiority in performance against
existing SSLDL methods.