Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Because this framework is relevant in many applications, they have received a lot of interest in both academia and industry. Among the existing techniques, self-training methods have undoubtedly attracted greater attention in recent years. These models are designed to find the decision boundary on low density regions without making additional assumptions about the data distribution, and use the unsigned output score of a learned classifier, or its margin, as an indicator of confidence. The working principle of self-training algorithms is to learn a classifier iteratively by assigning pseudo-labels to the set of unlabeled training samples with a margin greater than a certain threshold. The pseudo-labeled examples are then used to enrich the labeled training data and to train a new classifier in conjunction with the labeled training set. In this paper, we present self-training methods for binary and multi-class classification; as well as their variants and two related approaches, namely consistency-based approaches and transductive learning. We examine the impact of significant self-training features on various methods, using different general and image classification benchmarks, and we discuss our ideas for future research in self-training. To the best of our knowledge, this is the first thorough and complete survey on this subject.

翻译：半监督算法旨在从少量标记样本和大量未标记样本中学习预测函数。由于该框架在许多应用中具有相关性，学术界和工业界对此均给予了高度关注。在现有技术中，自训练方法无疑是近年来最受关注的。这些模型旨在寻找低密度区域的决策边界，而无需对数据分布做出额外假设，并利用已学习分类器的无符号输出分数或其间隔作为置信度指标。自训练算法的工作原理是通过迭代方式，为阈值以上间隔的未标记训练样本分配伪标签，从而学习分类器。这些伪标记样本随后被用于扩充已标记训练数据，并与已标记训练集共同训练新的分类器。本文介绍了用于二分类和多分类的自训练方法，以及它们的变体和两种相关方法，即基于一致性的方法和直推式学习。我们使用不同的通用和图像分类基准，分析了自训练重要特征对各种方法的影响，并探讨了未来自训练研究的思路。据我们所知，这是首份关于该主题的详尽完整综述。