  In recent years, significant progress has been made in face recognition,
which can be partially attributed to the availability of large-scale labeled
face datasets. However, since the faces in these datasets usually contain
limited degree and types of variation, the resulting trained models generalize
poorly to more realistic unconstrained face datasets. While collecting labeled
faces with larger variations could be helpful, it is practically infeasible due
to privacy and labor cost. In comparison, it is easier to acquire a large
number of unlabeled faces from different domains, which could be used to
regularize the learning of face representations. We present an approach to use
such unlabeled faces to learn generalizable face representations, where we
assume neither the access to identity labels nor domain labels for unlabeled
images. Experimental results on unconstrained datasets show that a small amount
of unlabeled data with sufficient diversity can (i) lead to an appreciable gain
in recognition performance and (ii) outperform the supervised baseline when
combined with less than half of the labeled data. Compared with the
state-of-the-art face recognition methods, our method further improves their
performance on challenging benchmarks, such as IJB-B, IJB-C and IJB-S.
