Class-Imbalanced Semi-Supervised Learning
This addresses a practical limitation in SSL for real-world datasets with imbalanced classes, though it is an incremental improvement over existing SSL techniques.
The paper tackles the problem of semi-supervised learning (SSL) with class-imbalanced data, proposing a Suppressed Consistency Loss (SCL) method that shows better performance than conventional methods, especially under severe imbalance and limited labeled data.
Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbalanced class distribution. In this paper, we introduce a task of class-imbalanced semi-supervised learning (CISSL), which refers to semi-supervised learning with class-imbalanced data. In doing so, we consider class imbalance in both labeled and unlabeled sets. First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods. Then we propose Suppressed Consistency Loss (SCL), a regularization method robust to class imbalance. Our method shows better performance than the conventional methods in the CISSL environment. In particular, the more severe the class imbalance and the smaller the size of the labeled data, the better our method performs.