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Dual-Training-Based Semi-Supervised Learning with Few Labels.

Authors :
Wu, Hao
Sun, Jun
Chen, Qidong
Source :
Applied Sciences (2076-3417); Jun2024, Vol. 14 Issue 12, p4993, 14p
Publication Year :
2024

Abstract

The continual expansion in the number of images poses a great challenge for the annotation of the data. Therefore, improving the model performance for image classification with limited labeled data has become an important problem to solve. To address the problem, we propose in this paper a simple and effective dual-training-based semi-supervised learning method for image classification. To enable the model to acquire more valuable information, we propose a dual training approach to enhance model training. Specifically, the model is trained with different augmented data at the same time with soft labels and hard labels, respectively. In addition, we propose a simple and effective weight generation method for generating the weight of samples during training to guide the model training. To further improve the model performance, we employ a projection layer at the end of the network to guide the self-learning of the model by minimizing the distance of features extracted from different layers. Finally, we evaluate the proposed approach on three benchmark image classification datasets. The experimental results demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
12
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
178157991
Full Text :
https://doi.org/10.3390/app14124993