Existing semi-supervised learning algorithms adopt pseudo-labeling and
consistency regulation techniques to introduce supervision signals for
unlabeled samples. To overcome the inherent limitation of threshold-based
pseudo-labeling, prior studies have attempted to align the confidence threshold
with the evolving learning status of the model, which is estimated through the
predictions made on the unlabeled data. In this paper, we further reveal that
classifier weights can reflect the differentiated learning status across
categories and consequently propose a class-specific adaptive threshold
mechanism. Additionally, considering that even the optimal threshold scheme
cannot resolve the problem of discarding unlabeled samples, a binary
classification consistency regulation approach is designed to distinguish
candidate classes from negative options for all unlabeled samples. By combining
the above strategies, we present a novel SSL algorithm named AllMatch, which
achieves improved pseudo-label accuracy and a 100\% utilization ratio for the
unlabeled data. We extensively evaluate our approach on multiple benchmarks,
encompassing both balanced and imbalanced settings. The results demonstrate
that AllMatch consistently outperforms existing state-of-the-art methods.

现有的半监督学习算法采用伪标记和一致性调节技术来引入未标记样本的监督信号。本文通过对未标记数据的预测来估计模型的学习状态，并提出了一种基于类别的自适应阈值机制，同时设计了一种二元分类一致性调节方法来区分未标记样本中的候选类别。通过结合上述策略，我们提出了一种名为 AllMatch 的新型 SSL 算法，其改进了伪标记准确性并实现了未标记数据的 100% 利用率。我们在多个基准数据集上广泛评估了我们的方法，包括平衡和不平衡设置。结果表明，AllMatch 始终优于现有的最先进方法。