  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.
