解决开放世界半监督学习中未知类误判问题
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
- 引入条件自标注与层级阈值机制，区分已知与未知类别
- 理论证明自标注估计无偏且可靠，提升分类稳定性
- 在已知和未知类别上均显著优于现有方法，适合真实场景
半监督学习（SSL）能有效利用未标注数据，但传统方法要求所有类别均有标注样本。开放世界半监督学习（OwSSL）则面临更现实挑战：未标注数据可能包含未见类别，导致未知类被错误归为已知类，降低准确率。为此，本文重新审视自监督与半监督学习中的自标注与一致性方法，提出OwMatch框架，结合条件自标注与开放世界层级阈值。通过严格的统计分析，理论证明该方法可保证未标注数据类别分布估计的无偏性与可靠性。大量实验证明，相比已有方法，OwMatch在已知类和未知类上均实现显著性能提升。代码已开源：https://github.com/niusj03/OwMatch。
原文摘要 · Abstract (English)
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.
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