Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915305129771008 |
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| author | Hu, Jiyu Zeng, Haijiang Tian, Zhen |
| author_facet | Hu, Jiyu Zeng, Haijiang Tian, Zhen |
| contents | In recent years, image classification, as a core task in computer vision, relies on high-quality labelled data, which restricts the wide application of deep learning models in practical scenarios. To alleviate the problem of insufficient labelled samples, semi-supervised learning has gradually become a research hotspot. In this paper, we construct a semi-supervised image classification model based on Generative Adversarial Networks (GANs), and through the introduction of the collaborative training mechanism of generators, discriminators and classifiers, we achieve the effective use of limited labelled data and a large amount of unlabelled data, improve the quality of image generation and classification accuracy, and provide an effective solution for the task of image recognition in complex environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_19522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning Hu, Jiyu Zeng, Haijiang Tian, Zhen Computer Vision and Pattern Recognition Machine Learning In recent years, image classification, as a core task in computer vision, relies on high-quality labelled data, which restricts the wide application of deep learning models in practical scenarios. To alleviate the problem of insufficient labelled samples, semi-supervised learning has gradually become a research hotspot. In this paper, we construct a semi-supervised image classification model based on Generative Adversarial Networks (GANs), and through the introduction of the collaborative training mechanism of generators, discriminators and classifiers, we achieve the effective use of limited labelled data and a large amount of unlabelled data, improve the quality of image generation and classification accuracy, and provide an effective solution for the task of image recognition in complex environments. |
| title | Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.19522 |