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.

通过引入一种名为TCBC的新方法，我们解决了传统半监督学习中的两个挑战：训练样本的不平衡分布导致模型偏向某些类别，以及未标记样本的分布未知且可能与已标记样本不同，在训练过程中进一步导致偏向类别的伪标签。我们通过利用参与训练样本的类别分布估计来纠正模型，使其学习在类别平衡先验下的样本后验概率，从而减轻模型固有的类别偏差。在此基础上，我们还估计了训练过程中当前模型参数的类别偏差，对未标记样本的伪标签进行二次修正，以尽量使不同类别的未标记样本的伪标签分配公平。通过对CIFAR10/100-LT、STL10-LT和大规模长尾数据集SUN397的大量实验，我们提供确凿证据，证明我们提出的TCBC方法可靠地提升了类别不平衡的半监督学习性能。

不平衡半监督学习的两次类别偏差校正