Kernel-Based Representation Alignment for Class Imbalanced Semi-Supervised Learning

Semi-supervised learning (SSL) offers a promising solution to the challenge of learning from limited labeled data by leveraging the potential of unlabeled data, thus circumventing the need for costly labeling efforts. However, common SSL methods often encounter domain shifts in many real-world scenarios, where class distribution is imbalanced. In order to make machine learning more robust to imbalanced datasets, it is imperative to ensure that consistent representations are learned for each class, regardless of the amount of data available. Therefore, we propose a straightforward yet effective kernel function mapping strategy to align the representations of each class in an infinite-dimensional space. Specifically, we employ a Gaussian kernel function to map the representations of unlabeled data to the centroids of labeled data, enabling similarity comparisons in the infinite-dimensional space. In this way, we are able to refine the predicted pseudo-labels at the representation level. To better handle class imbalance, we note that it is common to obtain a high recall but low precision for the majority classes and a high precision but low recall for the minority classes. A selective strategy is adopted for predictions corrected for the majority classes while maintaining confidence in the pseudo-labels assigned to the minority classes. Extensive evaluations on various benchmarks and training settings validate the superior performance of the proposed method compared to the existing relevant state-of-the-art approaches.