  Deep neural networks have been successfully applied to many real-world
applications. However, such successes rely heavily on large amounts of labeled
data that is expensive to obtain. Recently, many methods for semi-supervised
learning have been proposed and achieved excellent performance. In this study,
we propose a new EnAET framework to further improve existing semi-supervised
methods with self-supervised information. To our best knowledge, all current
semi-supervised methods improve performance with prediction consistency and
confidence ideas. We are the first to explore the role of {\bf self-supervised}
representations in {\bf semi-supervised} learning under a rich family of
transformations. Consequently, our framework can integrate the self-supervised
information as a regularization term to further improve {\it all} current
semi-supervised methods. In the experiments, we use MixMatch, which is the
current state-of-the-art method on semi-supervised learning, as a baseline to
test the proposed EnAET framework. Across different datasets, we adopt the same
hyper-parameters, which greatly improves the generalization ability of the
EnAET framework. Experiment results on different datasets demonstrate that the
proposed EnAET framework greatly improves the performance of current
semi-supervised algorithms. Moreover, this framework can also improve {\bf
supervised learning} by a large margin, including the extremely challenging
scenarios with only 10 images per class. The code and experiment records are
available in \url{https://github.com/maple-research-lab/EnAET}.
