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arXiv · 1905.03670

S4L: Self-Supervised Semi-Supervised Learning

Xiaohua Zhai·Avital Oliver·Alexander Kolesnikov·Lucas Beyer
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Abstract

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.

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Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer. 2019-05-09. S4L: Self-Supervised Semi-Supervised Learning. https://arxiv.org/abs/1905.03670

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