Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network
Abstract With the widespread adoption of Generative Adversarial Networks (GANs) in image generation and processing, enhancing their generation quality and visualization capabilities has become a prominent research focus. This study introduces a deep learning framework that integrates multi-scale inf...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07306-5 |
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| author | Qian Zhou |
| author_facet | Qian Zhou |
| author_sort | Qian Zhou |
| collection | DOAJ |
| description | Abstract With the widespread adoption of Generative Adversarial Networks (GANs) in image generation and processing, enhancing their generation quality and visualization capabilities has become a prominent research focus. This study introduces a deep learning framework that integrates multi-scale information chart generation with visualization enhancement to improve the performance of GAN-based image generation models across various domains. Based on the Self-Attention Generative Adversarial Network (SAGAN), which leverages self-attention mechanisms to capture long-range dependencies in images, the proposed approach significantly enhances image quality and detail representation. The framework incorporates a multi-scale feature extraction method to optimize the feature maps at each layer of the generative network. Experimental results demonstrate that SAGAN outperforms traditional GAN models in terms of image clarity, detail preservation, and visual effects. The proposed model achieves notable improvements in diversity and generalization, with a mutual information content of 0.91, clustering uniformity of 0.89, and inter-cluster dissimilarity of 0.92 on the CelebA dataset. Furthermore, in terms of image quality, SAGAN attains a Structural Similarity Index Measure (SSIM) of 0.94 and a Peak Signal-to-Noise Ratio (PSNR) of 30.1, surpassing traditional GANs by a significant margin. |
| format | Article |
| id | doaj-art-c8f2f94f6c9f475c888cbf9084416b72 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c8f2f94f6c9f475c888cbf9084416b722025-08-20T03:47:17ZengSpringerDiscover Applied Sciences3004-92612025-06-017712210.1007/s42452-025-07306-5Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial NetworkQian Zhou0College of Arts, Anhui Wenda University of Information EngineeringAbstract With the widespread adoption of Generative Adversarial Networks (GANs) in image generation and processing, enhancing their generation quality and visualization capabilities has become a prominent research focus. This study introduces a deep learning framework that integrates multi-scale information chart generation with visualization enhancement to improve the performance of GAN-based image generation models across various domains. Based on the Self-Attention Generative Adversarial Network (SAGAN), which leverages self-attention mechanisms to capture long-range dependencies in images, the proposed approach significantly enhances image quality and detail representation. The framework incorporates a multi-scale feature extraction method to optimize the feature maps at each layer of the generative network. Experimental results demonstrate that SAGAN outperforms traditional GAN models in terms of image clarity, detail preservation, and visual effects. The proposed model achieves notable improvements in diversity and generalization, with a mutual information content of 0.91, clustering uniformity of 0.89, and inter-cluster dissimilarity of 0.92 on the CelebA dataset. Furthermore, in terms of image quality, SAGAN attains a Structural Similarity Index Measure (SSIM) of 0.94 and a Peak Signal-to-Noise Ratio (PSNR) of 30.1, surpassing traditional GANs by a significant margin.https://doi.org/10.1007/s42452-025-07306-5Generative Adversarial Networks (GANs)Self-attention generative Adversarial Network (SAGAN)Multi-scale feature extractionData visualizationImage generation |
| spellingShingle | Qian Zhou Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network Discover Applied Sciences Generative Adversarial Networks (GANs) Self-attention generative Adversarial Network (SAGAN) Multi-scale feature extraction Data visualization Image generation |
| title | Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network |
| title_full | Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network |
| title_fullStr | Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network |
| title_full_unstemmed | Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network |
| title_short | Research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative Adversarial Network |
| title_sort | research on deep learning framework for multi scale information graph generation and visualization enhancement based on self attention generative adversarial network |
| topic | Generative Adversarial Networks (GANs) Self-attention generative Adversarial Network (SAGAN) Multi-scale feature extraction Data visualization Image generation |
| url | https://doi.org/10.1007/s42452-025-07306-5 |
| work_keys_str_mv | AT qianzhou researchondeeplearningframeworkformultiscaleinformationgraphgenerationandvisualizationenhancementbasedonselfattentiongenerativeadversarialnetwork |