Semi-supervised learning (SSL) algorithms struggle to perform well when exposed to imbalanced training data. In this scenario, the generated pseudo-labels can exhibit a bias towards the majority class, and models that employ these pseudo-labels can further amplify this bias. Here we investigate pseudo-labeling strategies for imbalanced SSL including pseudo-label refinement and threshold adjustment, through the lens of statistical analysis. We find that existing SSL algorithms which generate pseudo-labels using heuristic strategies or uncalibrated model confidence are unreliable when imbalanced class distributions bias pseudo-labels. To address this, we introduce SEmi-supervised learning with pseudo-label optimization based on VALidation data (SEVAL) to enhance the quality of pseudo-labelling for imbalanced SSL. We propose to learn refinement and thresholding parameters from a partition of the training dataset in a class-balanced way. SEVAL adapts to specific tasks with improved pseudo-labels accuracy and ensures pseudo-labels correctness on a per-class basis. Our experiments show that SEVAL surpasses state-of-the-art SSL methods, delivering more accurate and effective pseudo-labels in various imbalanced SSL situations. SEVAL, with its simplicity and flexibility, can enhance various SSL techniques effectively. The code is publicly available~\footnote{\url{https://github.com/ZerojumpLine/SEVAL}}.

通过统计分析视角，我们发现现有的半监督学习算法在不平衡的数据集上生成的伪标签不可靠，针对此问题，我们提出SEVAL，一种基于验证数据的半监督学习伪标签优化方法，通过在训练数据集的一个平衡分类划分上学习细化和阈值参数，SEVAL在特定任务中适应性强，具有改进的伪标签准确性和每个类别伪标签的正确性，实验证明SEVAL在各种不平衡半监督学习情境中超过了现有技术，能有效提升各种半监督学习技术的性能。

学习标签细化和阈值调整以应对不平衡的半监督学习