Work

ABM: Adaptive bias mitigation for class-imbalanced semi-supervised learning

Year: 2025

Type: article

Abstract: Class-imbalanced semi-supervised learning (CISSL) poses significant challenges in real-world scenarios, where limited labeled data and skewed class distributions jointly hinder model generalization, especially for minority classes. Existing CISSL methods predominantly focus on adjusting model outputs, often overlooking the crucial role of representation learning in addressing classifier bias. In this paper, we propose a feature-aware adaptive bias mitigation framework that effectively alleviates class imbalance while enhancing representation learning. Specifically, our approach leverages the batch-wise feature mean as an additional input to guide the learning process, enabling the model to calibrate its representations against an unbiased reference. We refine classifier predictions by adjusting logits based on batch-wise feature mean, and further apply post-hoc logit adjustment to correct residual response bias. This combination not only improves pseudo-label quality but also fosters balanced and robust feature learning. Extensive experiments on four benchmark datasets under various class distribution settings demonstrate that our method consistently outperforms state-of-the-art competitors, achieving superior balanced accuracy and robustness against distribution mismatches.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131617

Authors: Hongzhu Yi, Yue Cheng, Weiwei Xing, Xiang Wei

Institution: Beijing Jiaotong University

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $2,670