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31 May 2023 A semi-supervised learning for teacher-student model based on structural reparameterization
Yingying Kang, Hongfei Zhao, Yuting Tian
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Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270437 (2023) https://doi.org/10.1117/12.2680652
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China
Abstract
Semi-supervised learning (SSL) has attracted much interest for its ability to increase model performance using unlabeled data in recent years. The mainstreaming SSL frameworks are built on the teacher-student model. However, the structural reparameterization (SR) model has not been thoroughly studied in the teacher-student model. The SR technology enhances model capacity and improves performance during the training phase. Therefore, we introduce the SR model in the SSL framework, where the SR model is adopted to construct the teacher-student model for image classification. The consistency regularization is applied to the teacher and student models, and the teacher model’s weights are updated based on the exponential moving average (EMA) strategy. To verify the effectiveness of our approach, we conduct experiments using the CIFAR-10 and Food-101 datasets. Compared to the supervised learning of the SR model, our SSL framework has achieved better performance on these datasets.
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Yingying Kang, Hongfei Zhao, and Yuting Tian "A semi-supervised learning for teacher-student model based on structural reparameterization", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270437 (31 May 2023); https://doi.org/10.1117/12.2680652
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KEYWORDS
Machine learning
Convolution
Performance modeling
Image classification
Deep learning
Neural networks
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