swMATH ID: 41278
Software Authors: Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer
Description: S4L: Self-Supervised Semi-Supervised Learning. 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
Homepage: https://arxiv.org/abs/1905.03670
Source Code:  https://github.com/google-research/s4l
Dependencies: Python
Related Software: ImageNet; Adam; ViLBERT; VideoBERT; PyTorch; mixup; BERT; MixMatch; Python; FixMatch; ReMixMatch; CIFAR; MOTS; StyleGAN2; ResMLP; BRISK; EfficientDet; Face2Face; OpenPose; NIMA
Cited in: 1 Document

Cited by 1 Author

1 Szeliski, Richard

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