Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning
<|reference_start|>Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning: 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 (https://github.com/ZerojumpLine/SEVAL).<|reference_end|>
@article{li2024learning,
    title={Learning Label Refinement and Threshold Adjustment for Imbalanced
  Semi-Supervised Learning},
    author={Zeju Li, Ying-Qiu Zheng, Chen Chen, Saad Jbabdi},
    journal={arXiv preprint arXiv:2407.05370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05370},
    primaryClass={cs.LG}
}