  This work tackles the problem of semi-supervised learning of image
classifiers. Our main insight is that the field of semi-supervised learning can
benefit from the quickly advancing field of self-supervised visual
representation learning. Unifying these two approaches, we propose the
framework of self-supervised semi-supervised learning and use it to derive two
novel semi-supervised image classification methods. We demonstrate the
effectiveness of these methods in comparison to both carefully tuned baselines,
and existing semi-supervised learning methods. We then show that our approach
and existing semi-supervised methods can be jointly trained, yielding a new
state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels.
