S4L: Self-Supervised Semi-Supervised Learning
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 (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.
Cite
Text
Zhai et al. "S4L: Self-Supervised Semi-Supervised Learning." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00156Markdown
[Zhai et al. "S4L: Self-Supervised Semi-Supervised Learning." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/zhai2019iccv-s4l/) doi:10.1109/ICCV.2019.00156BibTeX
@inproceedings{zhai2019iccv-s4l,
title = {{S4L: Self-Supervised Semi-Supervised Learning}},
author = {Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00156},
url = {https://mlanthology.org/iccv/2019/zhai2019iccv-s4l/}
}