  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.
