Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes. This scenario leads to misclassification of unseen classes as known ones, consequently undermining classification accuracy. To overcome this challenge, this study revisits two methodologies from self-supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch, combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self-label assignment estimator with reliability. Comprehensive empirical analyses demonstrate that our method yields substantial performance enhancements across both known and unknown classes in comparison to previous studies. Code is available at https://github.com/niusj03/OwMatch.

本研究解决了开放世界半监督学习（OwSSL）中的一个重要问题，即未标记数据可能包含来自未见类别的样本，导致分类准确性下降。我们提出了一种名为OwMatch的框架，通过条件自标注和开放世界分层阈值相结合，确保自标注分配估计器的无偏性，实验结果表明该方法在已知和未知类别上均显著提高了性能。

OwMatch：具有一致性的条件自标注用于开放世界半监督学习