Combining neural-collapse feature alignment with classifier weight balancing improves closed-set and open-set accuracy for long-tailed medical image classification under semi-supervised learning.
To alleviate the effect of class imbalance, we employ feature regularization and classifier weight normalization
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Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
Combining neural-collapse feature alignment with classifier weight balancing improves closed-set and open-set accuracy for long-tailed medical image classification under semi-supervised learning.