  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. To
alleviate this issue, we formulate a convex optimization problem to softly
refine the pseudo-labels generated from the biased model, and develop a simple
algorithm, named Distribution Aligning Refinery of Pseudo-label (DARP) that
solves it provably and efficiently. Under various class-imbalanced
semi-supervised scenarios, we demonstrate the effectiveness of DARP and its
compatibility with state-of-the-art SSL schemes.
