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|a 10.1109/TIP.2020.3044220
|2 doi
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|a eng
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| 100 |
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|a Wang, Xiao
|e verfasserin
|4 aut
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| 245 |
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|a EnAET
|b A Self-Trained Framework for Semi-Supervised and Supervised Learning With Ensemble Transformations
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|c 2021
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 12.01.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many methods for semi-supervised learning have been proposed and achieved excellent performance. In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information. To our best knowledge, all current semi-supervised methods improve performance with prediction consistency and confidence ideas. We are the first to explore the role of self-supervised representations in semi-supervised learning under a rich family of transformations. Consequently, our framework can integrate the self-supervised information as a regularization term to further improve all current semi-supervised methods. In the experiments, we use MixMatch, which is the current state-of-the-art method on semi-supervised learning, as a baseline to test the proposed EnAET framework. Across different datasets, we adopt the same hyper-parameters, which greatly improves the generalization ability of the EnAET framework. Experiment results on different datasets demonstrate that the proposed EnAET framework greatly improves the performance of current semi-supervised algorithms. Moreover, this framework can also improve supervised learning by a large margin, including the extremely challenging scenarios with only 10 images per class. The code and experiment records are available in https://github.com/maple-research-lab/EnAET
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|a Journal Article
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1 |
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|a Kihara, Daisuke
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Jiebo
|e verfasserin
|4 aut
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| 700 |
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|a Qi, Guo-Jun
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 1639-1647
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|u http://dx.doi.org/10.1109/TIP.2020.3044220
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