  Pseudo-labeling has proven to be a promising semi-supervised learning (SSL)
paradigm. Existing pseudo-labeling methods commonly assume that the class
distributions of training data are balanced. However, such an assumption is far
from realistic scenarios and thus severely limits the performance of current
pseudo-labeling methods under the context of class-imbalance. To alleviate this
problem, we design a bias adaptive classifier that targets the imbalanced SSL
setups. The core idea is to automatically assimilate the training bias caused
by class imbalance via the bias adaptive classifier, which is composed of a
novel bias attractor and the original linear classifier. The bias attractor is
designed as a light-weight residual network and optimized through a bi-level
learning framework. Such a learning strategy enables the bias adaptive
classifier to fit imbalanced training data, while the linear classifier can
provide unbiased label prediction for each class. We conduct extensive
experiments under various imbalanced semi-supervised setups, and the results
demonstrate that our method can be applied to different pseudo-labeling models
and is superior to current state-of-the-art methods.
