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.

翻译：本文提出RankMatch，一种创新的半监督标签分布学习方法。针对标注数据有限的挑战，RankMatch有效利用少量标注样本与大量未标注数据，减少深度神经网络应用中人工标注的需求。具体而言，RankMatch引入一种集成学习启发的平均策略，从多个弱增强图像生成伪标签分布，这不仅稳定了预测结果，还增强了模型鲁棒性。此外，RankMatch整合成对相关性排序损失，捕捉复杂的标签间相关性，确保预测的标签分布与真实分布一致。我们建立了RankMatch的理论泛化界，并通过大量实验证明其性能优于现有半监督标签分布学习方法。