Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we provide a comprehensive review of 52 representative methods and offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences. In addition to the progress in the past few years, we further discuss some shortcomings of existing methods and provide some tentative heuristic solutions for solving these open problems.
翻译:深层半监督学习是一个快速增长的领域,具有一系列实际应用,本文件从模型设计和无人监督的损失功能的角度,对深层半监督学习方法的基本原理和最近的进展进行了全面调查。我们首先对深层半监督学习方法进行分类,对现有方法进行分类,包括深层遗传方法、一致性规范方法、图表方法、假标签方法和混合方法。然后,我们全面审查52种具有代表性的方法,并详细比较这些方法的损失类型、贡献和结构差异。除了过去几年的进展外,我们还进一步讨论了现有方法的一些缺点,为解决这些尚未解决的问题提供了一些暂时的超理论解决办法。