Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority classes, and 2) Biased pseudo-labels are used for training. It is difficult to appropriately re-balance the classifiers in SSL because the class distribution of an unlabeled set is often unknown and could be mismatched with that of a labeled set. We propose a novel class-imbalanced SSL algorithm called class-distribution-mismatch-aware debiasing (CDMAD). For each iteration of training, CDMAD first assesses the classifier's biased degree towards each class by calculating the logits on an image without any patterns (e.g., solid color image), which can be considered irrelevant to the training set. CDMAD then refines biased pseudo-labels of the base SSL algorithm by ensuring the classifier's neutrality. CDMAD uses these refined pseudo-labels during the training of the base SSL algorithm to improve the quality of the representations. In the test phase, CDMAD similarly refines biased class predictions on test samples. CDMAD can be seen as an extension of post-hoc logit adjustment to address a challenge of incorporating the unknown class distribution of the unlabeled set for re-balancing the biased classifier under class distribution mismatch. CDMAD ensures Fisher consistency for the balanced error. Extensive experiments verify the effectiveness of CDMAD.

翻译：基于伪标签的半监督学习算法在类别不平衡数据集上训练时面临两个级联挑战：1) 分类器倾向于偏向多数类，2) 有偏伪标签被用于训练。在半监督学习中难以适当地重新平衡分类器，因为未标注集的类别分布通常未知，且可能与标注集的类别分布不匹配。我们提出了一种新颖的类别不平衡半监督学习算法，称为类别分布失配感知的去偏方法（CDMAD）。在每次训练迭代中，CDMAD首先通过计算无任何模式的图像（如纯色图像）上的logits来评估分类器对每个类别的偏向程度，这些图像被视为与训练集无关。CDMAD随后通过确保分类器的中立性来修正基础半监督算法的有偏伪标签。CDMAD在基础半监督算法的训练过程中使用这些修正后的伪标签，以提高表征质量。在测试阶段，CDMAD类似地修正测试样本上的有偏类别预测。CDMAD可被视为后验logit调整的扩展，旨在解决在类别分布失配下，需结合未标注集未知类别分布以重新平衡有偏分类器的挑战。CDMAD确保了对平衡误差的Fisher一致性。大量实验验证了CDMAD的有效性。