  Positive-Unlabeled (PU) learning aims to learn a model with rare positive
samples and abundant unlabeled samples. Compared with classical binary
classification, the task of PU learning is much more challenging due to the
existence of many incompletely-annotated data instances. Since only part of the
most confident positive samples are available and evidence is not enough to
categorize the rest samples, many of these unlabeled data may also be the
positive samples. Research on this topic is particularly useful and essential
to many real-world tasks which demand very expensive labelling cost. For
example, the recognition tasks in disease diagnosis, recommendation system and
satellite image recognition may only have few positive samples that can be
annotated by the experts. These methods mainly omit the intrinsic hardness of
some unlabeled data, which can result in sub-optimal performance as a
consequence of fitting the easy noisy data and not sufficiently utilizing the
hard data. In this paper, we focus on improving the commonly-used nnPU with a
novel training pipeline. We highlight the intrinsic difference of hardness of
samples in the dataset and the proper learning strategies for easy and hard
data. By considering this fact, we propose first splitting the unlabeled
dataset with an early-stop strategy. The samples that have inconsistent
predictions between the temporary and base model are considered as hard
samples. Then the model utilizes a noise-tolerant Jensen-Shannon divergence
loss for easy data; and a dual-source consistency regularization for hard data
which includes a cross-consistency between student and base model for low-level
features and self-consistency for high-level features and predictions,
respectively.
