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MW-FixMatch: A class imbalance semi-supervised learning algorithm based on re-weighting
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DOI：10.1016/j.neucom.2025.129385.png)
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摘要
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Semi-supervised learning for image classification is an important research area in computer vision. These algorithms typically assume that both labeled and unlabeled datasets are class-balanced and share the same distribution. However, when there is an imbalance in the class distribution, it can significantly affect their performance. To address this issue, we propose MW-FixMatch, a novel approach that better adjusts the semi- supervised learning process in the presence of class imbalance. It utilizes a weight network to balance the contribution of labeled and unlabeled data, and the parameters of this network are learned from a class- balanced sampled set. We tested our approach on several publicly available image datasets with class imbalance and consistently achieved superior results across multiple experiments.
Keyword:
Semi-supervised learning
Class imbalance
Meta-learning
Image classification
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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