  Positive and Unlabeled (PU) learning, a binary classification model trained
with only positive and unlabeled data, generally suffers from overfitted risk
estimation due to inconsistent data distributions. To address this, we
introduce a pseudo-supervised PU learning framework (PSPU), in which we train
the PU model first, use it to gather confident samples for the pseudo
supervision, and then apply these supervision to correct the PU model's weights
by leveraging non-PU objectives. We also incorporate an additional consistency
loss to mitigate noisy sample effects. Our PSPU outperforms recent PU learning
methods significantly on MNIST, CIFAR-10, CIFAR-100 in both balanced and
imbalanced settings, and enjoys competitive performance on MVTecAD for
industrial anomaly detection.
