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。

AllMatch: 充分利用所有未标记数据进行半监督学习

AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning

Conventional methods in semi-supervised learning (SSL) often face challenges
related to limited data utilization, mainly due to their reliance on
threshold-based techniques for selecting high-confidence unlabeled data during
training. Various efforts (e.g., FreeMatch) have been made to enhance data
utilization by tweaking the thresholds, yet none have managed to use 100% of
the available data. To overcome this limitation and improve SSL performance, we
introduce \algo, a novel algorithm that fully utilizes unlabeled data to boost
semi-supervised learning. \algo integrates a self-supervised learning strategy,
i.e., Masked Autoencoder (MAE), that uses all available data to enforce the
visual representation learning. This enables the SSL algorithm to leverage all
available data, including samples typically filtered out by traditional
methods. In addition, we propose a synthetic data training approach to further
increase data utilization and improve generalization. These innovations lead
\algo to achieve state-of-the-art results on challenging datasets. For
instance, on CIFAR-100 with 2 labels per class, STL-10 with 4 labels per class,
and Euro-SAT with 2 labels per class, \algo achieves low error rates of 18.71%,
9.47%, and 3.07%, respectively. The code will be made publicly available.

半监督学习中存在数据利用有限的挑战，本研究介绍了一种全新的算法，利用未标记数据来提升半监督学习的性能，并取得了最先进的结果。

MaskMatch：基于 Mask 自编码特征学习的半监督学习增强

MaskMatch: Boosting Semi-Supervised Learning Through Mask  Autoencoder-Driven Feature Learning

Semi-supervised learning (SSL) algorithm is a setup built upon a realistic
assumption that access to a large amount of labeled data is tough. In this
study, we present a generalized framework, named SCAR, standing for Selecting
Clean samples with Adversarial Robustness, for improving the performance of
recent SSL algorithms. By adversarially attacking pre-trained models with
semi-supervision, our framework shows substantial advances in classifying
images. We introduce how adversarial attacks successfully select high-confident
unlabeled data to be labeled with current predictions. On CIFAR10, three recent
SSL algorithms with SCAR result in significantly improved image classification.

SCAR 是一种通用的框架，以对抗健壮性选择清洁样本，从而提高最近的半监督学习算法在图像分类方面的性能。通过对预训练模型进行对抗攻击，我们的框架成功地选择了高置信度的未标记数据以便与当前预测进行标记。在 CIFAR10 上，使用 SCAR 的三个最新的半监督学习算法大大提高了图像分类效果。