发表机构
School of Statistics, East China Normal University; School of Computer Science, East China Normal University（统计学系，华东师范大学; 计算机科学系，华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究半监督学习中伪标签阈值τ，建立统一理论框架解释其作用，将τ视为可更新参数优化得到MTSSL，实验证明MTSSL性能优越，支持理论框架，表明未来SSL算法设计中τ选择可放宽。
AI 中文摘要
大量半监督学习（SSL）算法在选择伪标签时会遇到阈值τ。不同SSL算法中τ的值因学习视角而异，但性能可能相似。这促使我们建立统一理论框架来解释τ在SSL中的作用。我们从统计学上解释了无监督损失受正确和错误伪标签的独立影响，而τ调整它们的数量以平衡相应误差项。这种内在权衡表明SSL在不同τ值下可达到相同损失，训练时可能无需精确的最优τ值。因此，我们将τ视为可更新参数并通过微分优化它，新策略称为元阈值半监督学习（MTSSL）。大量实验证明了MTSSL的优越性能。我们观察到即使τ值差异很大，SSL算法的准确率曲线也能完全重叠，这支持了我们的理论框架，并表明在未来SSL算法设计中可以放宽对τ的选择。
英文摘要
A large body of Semi-supervised Learning~(SSL) algorithms encounter the threshold $τ$ to select pseudo-labels. The value of $τ$ across different SSL algorithms can vary depending on the learning perspective, yet they may achieve similar performance. It motivates us to establish a unified theoretical framework to explain the role of $τ$ in SSL. We statistically explained that the unsupervised loss is affected independently by correct and incorrect pseudo-labels, while $τ$ adjusts their numbers to balance the corresponding error term. This inherent trade-off indicates that SSL can reach the same loss with varying $τ$, precise optimal values of $τ$ during training may be unnecessary. With this, we treat $τ$ as an updatable parameter and optimize it via differentiation; the new policy is named \textbf{Meta-Thresholding Semi-Supervised Learning (MTSSL)}. Extensive experiments demonstrate the superior performance of MTSSL. We observe that the accuracy curves of SSL algorithms can overlap completely even when the values of $τ$ differ significantly, which supports our theoretical framework and indicates that the selection of $τ$ can be relaxed in the future design of SSL algorithms.
