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.

本文提出一种称为类别不平衡半监督学习（CISSL）的任务，介绍了现有的类别不平衡 SSL 方法，并提出了一种所谓的压抑一致性损失（SCL）的正则化方法，该方法对于类别不平衡情况具有鲁棒性，在 CISSL 环境下比传统方法表现更好。