S4L: Self-Supervised Semi-Supervised Learning
| Autor: | Lucas Beyer, Avital Oliver, Xiaohua Zhai, Alexander Kolesnikov |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Contextual image classification business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Semi-supervised learning 010501 environmental sciences 01 natural sciences Field (computer science) Machine Learning (cs.LG) Image (mathematics) Visualization ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business Feature learning 0105 earth and related environmental sciences |
| Zdroj: | ICCV |
| DOI: | 10.1109/iccv.2019.00156 |
| Popis: | 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. Comment: All four authors contributed equally |
| Databáze: | OpenAIRE |
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