  Due to the advantages of leveraging unlabeled data and learning meaningful
representations, semi-supervised learning and contrastive learning have been
progressively combined to achieve better performances in popular applications
with few labeled data and abundant unlabeled data. One common manner is
assigning pseudo-labels to unlabeled samples and selecting positive and
negative samples from pseudo-labeled samples to apply contrastive learning.
However, the real-world data may be imbalanced, causing pseudo-labels to be
biased toward the majority classes and further undermining the effectiveness of
contrastive learning. To address the challenge, we propose Contrastive Learning
with Augmented Features (CLAF). We design a class-dependent feature
augmentation module to alleviate the scarcity of minority class samples in
contrastive learning. For each pseudo-labeled sample, we select positive and
negative samples from labeled data instead of unlabeled data to compute
contrastive loss. Comprehensive experiments on imbalanced image classification
datasets demonstrate the effectiveness of CLAF in the context of imbalanced
semi-supervised learning.
