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.

翻译：本文对近期的半监督学习方法及相关研究进行了系统性综述。尽管神经网络在各种应用中取得了显著成功，但仍面临一些严峻挑战，其中最主要的是对大量标注数据的需求。因此，半监督学习——一种利用少量标注数据和大量未标注数据训练模型（如深度神经网络）的学习范式——正变得日益重要。基于半监督学习的核心假设（流形假设、聚类假设和连续性假设），本文系统回顾了近年来的半监督学习方法。特别地，我们重点讨论了在深度神经网络框架下实现半监督学习的技术路径。此外，本文首先根据核心思想对现有研究进行分类阐述，继而详细解析了整合上述思想的综合性方法。