Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
Lan Li Bowen Tao Lu Han De-chuan Zhan Han-jia Ye
Differing from traditional semi-supervised learning, class-imbalanced
semi-supervised learning presents two distinct challenges: (1) The imbalanced
distribution of training samples leads to model bias towards certain classes,
and (2) the distribution of unlabeled samples is unknown and potentially
distinct from that of labeled samples, which further contributes to class bias
in the pseudo-labels during training. To address these dual challenges, we
introduce a novel approach called \textbf{T}wice \textbf{C}lass \textbf{B}ias
\textbf{C}orrection (\textbf{TCBC}). We begin by utilizing an estimate of the
class distribution from the participating training samples to correct the
model, enabling it to learn the posterior probabilities of samples under a
class-balanced prior. This correction serves to alleviate the inherent class
bias of the model. Building upon this foundation, we further estimate the class
bias of the current model parameters during the training process. We apply a
secondary correction to the model's pseudo-labels for unlabeled samples, aiming
to make the assignment of pseudo-labels across different classes of unlabeled
samples as equitable as possible. Through extensive experimentation on
CIFAR10/100-LT, STL10-LT, and the sizable long-tailed dataset SUN397, we
provide conclusive evidence that our proposed TCBC method reliably enhances the
performance of class-imbalanced semi-supervised learning.