Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning

• URL: http://arxiv.org/abs/2407.05370v1
• Date: Sun, 7 Jul 2024 13:46:22 GMT
• Title: Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning
• Authors: Zeju Li, Ying-Qiu Zheng, Chen Chen, Saad Jbabdi,
• Abstract summary: Semi-supervised learning algorithms struggle when exposed to imbalanced training data. We introduce SEmi-supervised learning with pseudo-label optimization based on VALidation data. SEVAL adapts to specific tasks with improved pseudo-labels accuracy and ensures pseudo-labels correctness on a per-class basis.
• Score: 6.904448748214652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semi-supervised learning (SSL) algorithms struggle to perform well when exposed to imbalanced training data. In this scenario, the generated pseudo-labels can exhibit a bias towards the majority class, and models that employ these pseudo-labels can further amplify this bias. Here we investigate pseudo-labeling strategies for imbalanced SSL including pseudo-label refinement and threshold adjustment, through the lens of statistical analysis. We find that existing SSL algorithms which generate pseudo-labels using heuristic strategies or uncalibrated model confidence are unreliable when imbalanced class distributions bias pseudo-labels. To address this, we introduce SEmi-supervised learning with pseudo-label optimization based on VALidation data (SEVAL) to enhance the quality of pseudo-labelling for imbalanced SSL. We propose to learn refinement and thresholding parameters from a partition of the training dataset in a class-balanced way. SEVAL adapts to specific tasks with improved pseudo-labels accuracy and ensures pseudo-labels correctness on a per-class basis. Our experiments show that SEVAL surpasses state-of-the-art SSL methods, delivering more accurate and effective pseudo-labels in various imbalanced SSL situations. SEVAL, with its simplicity and flexibility, can enhance various SSL techniques effectively. The code is publicly available~\footnote{\url{https://github.com/ZerojumpLine/SEVAL}}.

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