发表机构
Beijing Jiaotong University; Communication University of China（北京交通大学; 中国传媒大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对半监督学习中教师-学生框架的参数耦合和损失不平衡问题，提出独立参数空间的先锋学生辅助分支，定期回传知识，作为即插即用模块提升主流SSL方法。
AI 中文摘要
半监督学习（SSL）依赖于两个核心机制：在教师-学生（T-S）框架下的自训练，以及标记和无标记损失的联合优化。尽管这些机制有效，我们发现它们都引入了不同的优化病理。首先，参数耦合强制教师和学生之间的严格同步，其中对学生施加的强正则化会降低教师的拟合能力，从而限制了允许的泛化强度。其次，标记和无标记损失之间梯度更新一致性的不平衡，驱动共享参数过早收敛到标记主导的局部最小值，为全局优化创造了瓶颈。为了解决这两个问题，我们提出了先锋学生（PiS），一个在独立参数空间中运行的辅助分支，并定期将积累的知识转移回T-S模型。大量实验表明，PiS是一个通用的即插即用模块，能够持续改进主流的SSL方法。
英文摘要
Semi-supervised learning (SSL) relies on two core mechanisms: self-training under the Teacher-Student (T-S) framework and joint optimization of labeled and unlabeled losses. Despite their effectiveness, we find both mechanisms introduce distinct optimization pathologies. First, parameter coupling enforces strict synchronization between teacher and student, where strong regularization on the student degrades the teacher's fitting ability, thereby limiting the permissible generalization intensity. Second, the imbalance in gradient update consistency between labeled and unlabeled losses drives the shared parameters to prematurely converge to labeled-dominated local minima, creating a bottleneck for global optimization. To address both issues, we propose the Pioneer Student (PiS), an auxiliary branch that operates in an independent parameter space and periodically transfers accumulated knowledge back to the T-S model. Extensive experiments show that PiS is a universal plug-and-play module that consistently improves mainstream SSL methods.
