  In the early history of positive-unlabeled (PU) learning, the sample
selection approach, which heuristically selects negative (N) data from U data,
was explored extensively. However, this approach was later dominated by the
importance reweighting approach, which carefully treats all U data as N data.
May there be a new sample selection method that can outperform the latest
importance reweighting method in the deep learning age? This paper is devoted
to answering this question affirmatively---we propose to label large-loss U
data as P, based on the memorization properties of deep networks. Since P data
selected in such a way are biased, we develop a novel learning objective that
can handle such biased P data properly. Experiments confirm the superiority of
the proposed method.
