标题： SST：基于自适应阈值的自我训练在半监督学习中的应用 显示英文标题

标题： SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning

摘要： 神经网络在有监督学习中表现出色，得益于大量高质量标注数据的支持。然而，在现实场景中获取此类数据成本高昂且耗时费力。半监督学习（SSL）为此问题提供了一种解决方案。近期研究如Semi-ViT和Noisy Student，采用一致性正则化或伪标签方法，取得了显著成就。然而，它们仍面临挑战，尤其是在由于依赖固定阈值而难以准确选择足够高质量伪标签的问题上。最近的方法如FlexMatch和FreeMatch引入了灵活或自适应阈值技术，极大地推动了SSL研究的发展。然而，它们每次迭代更新阈值的过程被认为耗时、计算密集且可能不必要的。为了解决这些问题，我们提出了带自适应阈值的自我训练（SST），这是一种新颖、有效且高效的SSL框架。SST引入了一种创新的自适应阈值机制，根据模型的学习进度自适应调整类别特定的阈值。该机制确保高质量伪标签数据的选择，减轻了伪标签不准确和确认偏差的风险。广泛的实验表明，SST在各种架构和数据集上实现了最先进的性能，具有卓越的效率、泛化能力和可扩展性。Semi-SST-ViT-Huge在竞争性的ImageNet-1K SSL基准测试中取得了最佳结果，使用仅1%/10%标记数据分别达到80.7%/84.9%的Top-1准确率。与完全监督的DeiT-III-ViT-Huge相比，后者使用100%标记数据达到84.8%的Top-1准确率，我们的方法仅用10%标记数据就展示了优越的性能。 显示英文摘要

摘要： Neural networks have demonstrated exceptional performance in supervised learning, benefiting from abundant high-quality annotated data. However, obtaining such data in real-world scenarios is costly and labor-intensive. Semi-supervised learning (SSL) offers a solution to this problem. Recent studies, such as Semi-ViT and Noisy Student, which employ consistency regularization or pseudo-labeling, have demonstrated significant achievements. However, they still face challenges, particularly in accurately selecting sufficient high-quality pseudo-labels due to their reliance on fixed thresholds. Recent methods such as FlexMatch and FreeMatch have introduced flexible or self-adaptive thresholding techniques, greatly advancing SSL research. Nonetheless, their process of updating thresholds at each iteration is deemed time-consuming, computationally intensive, and potentially unnecessary. To address these issues, we propose Self-training with Self-adaptive Thresholding (SST), a novel, effective, and efficient SSL framework. SST introduces an innovative Self-Adaptive Thresholding (SAT) mechanism that adaptively adjusts class-specific thresholds based on the model's learning progress. SAT ensures the selection of high-quality pseudo-labeled data, mitigating the risks of inaccurate pseudo-labels and confirmation bias. Extensive experiments demonstrate that SST achieves state-of-the-art performance with remarkable efficiency, generalization, and scalability across various architectures and datasets. Semi-SST-ViT-Huge achieves the best results on competitive ImageNet-1K SSL benchmarks, with 80.7% / 84.9% Top-1 accuracy using only 1% / 10% labeled data. Compared to the fully-supervised DeiT-III-ViT-Huge, which achieves 84.8% Top-1 accuracy using 100% labeled data, our method demonstrates superior performance using only 10% labeled data.