  The author of 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 few formidable constraints
including the need for a large amount of labeled data. Therefore,
semi-supervised learning, which is a learning scheme in which the 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, and then the holistic approaches that unify the
aforementioned ideas are detailed.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition