  Conventional methods in semi-supervised learning (SSL) often face challenges
related to limited data utilization, mainly due to their reliance on
threshold-based techniques for selecting high-confidence unlabeled data during
training. Various efforts (e.g., FreeMatch) have been made to enhance data
utilization by tweaking the thresholds, yet none have managed to use 100% of
the available data. To overcome this limitation and improve SSL performance, we
introduce \algo, a novel algorithm that fully utilizes unlabeled data to boost
semi-supervised learning. \algo integrates a self-supervised learning strategy,
i.e., Masked Autoencoder (MAE), that uses all available data to enforce the
visual representation learning. This enables the SSL algorithm to leverage all
available data, including samples typically filtered out by traditional
methods. In addition, we propose a synthetic data training approach to further
increase data utilization and improve generalization. These innovations lead
\algo to achieve state-of-the-art results on challenging datasets. For
instance, on CIFAR-100 with 2 labels per class, STL-10 with 4 labels per class,
and Euro-SAT with 2 labels per class, \algo achieves low error rates of 18.71%,
9.47%, and 3.07%, respectively. The code will be made publicly available.
