MTSSL: Meta-Thresholding Semi-Supervised Learning
Shuyang Liu, Ziang Zeng, Ruiqiu Zheng, Jiazheng Wang, Zechen Liu, Wenxi Li, Zhou Yu
stat.ML, cs.LG
Submitted: 2026-07-17
License: http://creativecommons.org/licenses/by/4.0/
The gist: A large body of Semi-supervised Learning (SSL) algorithms encounter the threshold tau to select pseudo-labels.
Terminology
Abstract
A large body of Semi-supervised Learning (SSL) algorithms encounter the threshold tau to select pseudo-labels. The value of tau 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 tau in SSL. We statistically explained that the unsupervised loss is affected independently by correct and incorrect pseudo-labels, while tau adjusts their numbers to balance the corresponding error term. This inherent trade-off indicates that SSL can reach the same loss with varying tau, precise optimal values of tau during training may be unnecessary. With this, we treat tau as an updatable parameter and optimize it via differentiation; the new policy is named 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 tau differ significantly, which supports our theoretical framework and indicates that the selection of tau can be relaxed in the future design of SSL algorithms.
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