  Learning from positive and unlabeled data (PU learning) is prevalent in
practical applications where only a couple of examples are positively labeled.
Previous PU learning studies typically rely on existing samples such that the
data distribution is not extensively explored. In this work, we propose a
simple yet effective data augmentation method, coined~\algo, based on
\emph{consistency regularization} which provides a new perspective of using PU
data. In particular, the proposed~\algo~incorporates supervised and
unsupervised consistency training to generate augmented data. To facilitate
supervised consistency, reliable negative examples are mined from unlabeled
data due to the absence of negative samples. Unsupervised consistency is
further encouraged between unlabeled datapoints. In addition,~\algo~reduces
margin loss between positive and unlabeled pairs, which explicitly optimizes
AUC and yields faster convergence. Finally, we conduct a series of studies to
demonstrate the effectiveness of consistency regularization. We examined three
kinds of reliable negative mining methods. We show that~\algo~achieves an
averaged improvement of classification error from 16.49 to 13.09 on the
CIFAR-10 dataset across different positive data amount.
