  Semi-supervised semantic segmentation learns from small amounts of labelled
images and large amounts of unlabelled images, which has witnessed impressive
progress with the recent advance of deep neural networks. However, it often
suffers from severe class-bias problem while exploring the unlabelled images,
largely due to the clear pixel-wise class imbalance in the labelled images.
This paper presents an unbiased subclass regularization network (USRN) that
alleviates the class imbalance issue by learning class-unbiased segmentation
from balanced subclass distributions. We build the balanced subclass
distributions by clustering pixels of each original class into multiple
subclasses of similar sizes, which provide class-balanced pseudo supervision to
regularize the class-biased segmentation. In addition, we design an
entropy-based gate mechanism to coordinate learning between the original
classes and the clustered subclasses which facilitates subclass regularization
effectively by suppressing unconfident subclass predictions. Extensive
experiments over multiple public benchmarks show that USRN achieves superior
performance as compared with the state-of-the-art.
