Debiased Label Enhancement for Class-Imbalanced Semi-Supervised Learning
Yifan WANG , Biao LIU , Xin GENG , Ning XU
In class-imbalanced semi-supervised learning, the goal is to leverage abundant unlabeled data when labeled examples are scarce in a class-imbalanced setting. Classifiers of pseudo-label-based algorithms tend to become biased and suffer from degraded representation quality due to the utilization of skewed pseudo-labels for training. Previous pseudo-label-based algorithms employ the classifier itself to generate pseudo-labels for unlabeled data, leading to suboptimal performance on imbalanced tasks. The classifier is optimized to achieve uniform accuracy across all classes, mitigating the bias toward majority classes, while pseudo-labeling strives to accurately annotate the training unlabeled data in a class-imbalanced distribution. This misalignment causes confirmation bias, reinforcing bias in the pseudo-labeling process. To address this issue, we propose a novel semi-supervised framework that disentangles pseudo-label generation from the classification task via designing a dedicated pseudo-label generator to align the class distributions between labeled and unlabeled data. Specifically, we alternately train the pseudo-label generator and the predictive model, where the pseudo-label generator is trained on a debiased label enhancement objective, and the predictive model then leverages these pseudo-labels along with class-level debiasing. Experiments on the imbalanced benchmark datasets validate the effectiveness of the proposed framework.
Class-imbalanced semi-supervised learning / label enhancement
Higher Education Press 2026
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