Papers › S4L: Self-Supervised Semi-Supervised Learning

S4L: Self-Supervised Semi-Supervised Learning

9 May 2019ICCV 2019 10arXiv:1905.03670archive 2025-07-28

Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer

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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