Strong Views for Representation, Weak Views for Classification: Rethinking Augmentation in Class-Imbalanced Semi-Supervised Learning

Class-imbalanced semi-supervised learning (CISSL) aims to leverage unlabeled data for both effective representation learning and balanced classification. Existing methods commonly use weak-to-strong augmentation consistency for both objectives, but we show that the appropriate augmentation strength should instead be chosen according to the learning objective. Strong augmentation promotes invariant representation learning, but it also shifts class-conditional means toward their center in feature space substantially more than weak augmentation, reducing the distance between classes. Under class imbalance, these smaller distances amplify the imbalance-induced boundary shift toward tail classes, shrinking their decision regions. These findings motivate a role-separated principle: use strong augmentation to learn representations, but weak augmentation to estimate the decision boundary. We instantiate this principle with the Weak-View Classifier (WVC), which estimates the final decision boundary from weakly augmented samples, where the distances between class means are better preserved. To improve the pseudo-labels used for decision-boundary estimation, we further introduce Reference-Guided Logit Adjustment (RGLA), which uses class prototypes as a reference to adapt the strength of logit adjustment without knowledge of the unlabeled class distribution. Across four CISSL benchmarks and multiple base SSL algorithms, our approach consistently improves decision-boundary estimation and achieves competitive or superior performance across diverse settings.