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S4L: Self-Supervised Semi-Supervised Learning
Open accessAbstract
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.
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Metadata
- Authors
- Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1476-1485
- OpenAlex
- W2946856970
- Cited by
- 750
- References
- 8
- FWCI
- 42.97
- Type
- conference paper
- Language
- en
- Open access
- green
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