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Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
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.
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
For each pseudo-labeled sample, we select positive and negative samples from labeled data instead of unlabeled data to compute contrastive loss.
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