In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model's representation network is more reliable than the classification network. Additionally, label generation methods based on model predictions often show poor adaptability across different datasets, necessitating customization of the classification network. Therefore, we propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. We also introduce an adaptive method for selecting hyperparameters in HDL, enhancing its versatility. Moreover, HDL can be combined with general image encoders (e.g., CLIP) to serve as a fundamental data processing module. We extract embeddings from datasets with class-balanced and long-tailed distributions using pre-trained semi-supervised models. Subsequently, samples are re-labeled using HDL, and the re-labeled samples are used to further train the semi-supervised models. Experiments demonstrate improved model performance, validating the motivation that representation networks are more reliable than classifiers or predictors. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.

翻译：在半监督学习中，基于置信学习生成伪标签的方法已被广泛提出。然而，越来越多的研究发现，当面对含噪声和有偏数据时，模型的表示网络比分类网络更可靠。此外，基于模型预测的标签生成方法在不同数据集上往往表现出较差的适应性，需要定制化分类网络。因此，我们提出了一种不依赖于模型预测、利用图像嵌入生成样本标签的分层动态标注（HDL）算法。我们还引入了一种自适应选择HDL超参数的方法，增强了其通用性。此外，HDL可与通用图像编码器（如CLIP）结合，作为基础数据处理模块。我们使用预训练的半监督模型从类别平衡和长尾分布的数据集中提取嵌入，随后利用HDL对样本进行重新标注，并进一步使用重新标注的样本训练半监督模型。实验表明模型性能得到提升，验证了表示网络比分类器或预测器更可靠的动机。我们的方法有潜力改变半监督学习中伪标签生成的范式。