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.

通过引入 RankMatch 方法，本文提出了一种创新的半监督标签分布学习 (SSLDL) 方法，可以在深度神经网络 (DNN) 应用中有效利用少量有标签的样本和大量无标签的数据，减少手动标记的需求。RankMatch 方法通过集成学习策略和弱增强图像的伪标签分布，稳定预测结果并增强模型的鲁棒性；同时，它还引入一种配对相关性排序的损失函数，捕捉标签之间的复杂关联，确保预测的标签分布与实际情况相符。我们还给出了 RankMatch 的理论泛化边界，并通过大量实验证明它在性能上优于现有的 SSLDL 方法。