Semi-supervised learning can significantly boost model performance by
leveraging unlabeled data, particularly when labeled data is scarce. However,
real-world unlabeled data often contain unseen-class samples, which can hinder
the classification of seen classes. To address this issue, mainstream safe SSL
methods suggest detecting and discarding unseen-class samples from unlabeled
data. Nevertheless, these methods typically employ a single-model strategy to
simultaneously tackle both the classification of seen classes and the detection
of unseen classes. Our research indicates that such an approach may lead to
conflicts during training, resulting in suboptimal model optimization. Inspired
by this, we introduce a novel framework named Diverse Teacher-Students
(\textbf{DTS}), which uniquely utilizes dual teacher-student models to
individually and effectively handle these two tasks. DTS employs a novel
uncertainty score to softly separate unseen-class and seen-class data from the
unlabeled set, and intelligently creates an additional ($K$+1)-th class
supervisory signal for training. By training both teacher-student models with
all unlabeled samples, DTS can enhance the classification of seen classes while
simultaneously improving the detection of unseen classes. Comprehensive
experiments demonstrate that DTS surpasses baseline methods across a variety of
datasets and configurations. Our code and models can be publicly accessible on
the link this https URL

半监督学习能够通过利用未标记的数据显著提高模型性能，然而，现实世界的未标记数据往往包含看不见类别的样本，这可能阻碍对已知类别的分类。为解决这个问题，我们引入了一个名为 Diverse Teacher-Students (DTS) 的新型框架，该框架利用双师生模型分别有效处理这两个任务，通过训练两个师生模型使用所有未标记样本，DTS 能够提高已知类别的分类性能，同时改善看不见类别的检测。