  This work proposes an overview of the recent semi-supervised learning
approaches and related works. Despite the remarkable success of neural networks
in various applications, there exist a few formidable constraints, including
the need for a large amount of labeled data. Therefore, semi-supervised
learning, which is a learning scheme in which scarce labels and a larger amount
of unlabeled data are utilized to train models (e.g., deep neural networks), is
getting more important. Based on the key assumptions of semi-supervised
learning, which are the manifold assumption, cluster assumption, and continuity
assumption, the work reviews the recent semi-supervised learning approaches. In
particular, the methods in regard to using deep neural networks in a
semi-supervised learning setting are primarily discussed. In addition, the
existing works are first classified based on the underlying idea and explained,
then the holistic approaches that unify the aforementioned ideas are detailed.
