  Semi-supervised learning (SSL) is a practical challenge in computer vision.
Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of
The Art (SOTA) performances in SSL. These approaches employ a
threshold-to-pseudo-label (T2L) process to generate PLs by truncating the
confidence scores of unlabeled data predicted by the self-training method.
However, self-trained models typically yield biased and high-variance
predictions, especially in the scenarios when a little labeled data are
supplied. To address this issue, we propose a lightweight channel-based
ensemble method to effectively consolidate multiple inferior PLs into the
theoretically guaranteed unbiased and low-variance one. Importantly, our
approach can be readily extended to any SSL framework, such as FixMatch or
FreeMatch. Experimental results demonstrate that our method significantly
outperforms state-of-the-art techniques on CIFAR10/100 in terms of
effectiveness and efficiency.
