S4L 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; ReMixMatch; FixMatch; MixMatch; Python; CIFAR; SynSin; Make3D; PIFuHD; BRISK; Qsplat; KiloNeRF; SinGAN; LoFTR Cited in: 1 Publication Cited by 1 Author 1 Szeliski, Richard Cited in 1 Serial 1 Texts in Computer Science Cited in 1 Field 1 Computer science (68-XX) Citations by Year