S4L: Self-Supervised Semi-Supervised Learning

Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1476-1485

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 (S4L) 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 S4L 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.