Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

17 Jul 2020Jaehyung KimYoungbum HurSejun ParkEunho YangSung Ju HwangJinwoo Shin

While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that training class distributions are balanced. However, these SSL algorithms trained under imbalanced class distributions can severely suffer when generalizing to a balanced testing criterion, since they utilize biased pseudo-labels of unlabeled data toward majority classes... (read more)

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