  In this paper, we address a complex but practical scenario in semi-supervised
learning (SSL) named open-set SSL, where unlabeled data contain both
in-distribution (ID) and out-of-distribution (OOD) samples. Unlike previous
methods that only consider ID samples to be useful and aim to filter out OOD
ones completely during training, we argue that the exploration and exploitation
of both ID and OOD samples can benefit SSL. To support our claim, i) we propose
a prototype-based clustering and identification algorithm that explores the
inherent similarity and difference among samples at feature level and
effectively cluster them around several predefined ID and OOD prototypes,
thereby enhancing feature learning and facilitating ID/OOD identification; ii)
we propose an importance-based sampling method that exploits the difference in
importance of each ID and OOD sample to SSL, thereby reducing the sampling bias
and improving the training. Our proposed method achieves state-of-the-art in
several challenging benchmarks, and improves upon existing SSL methods even
when ID samples are totally absent in unlabeled data.
