SUGAN: A Stable U-Net Based Generative Adversarial Network
As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based GAN (U-Net GAN), a recently developed approach, ca...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7338 |
| _version_ | 1797581922146189312 |
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| author | Shijie Cheng Lingfeng Wang Min Zhang Cheng Zeng Yan Meng |
| author_facet | Shijie Cheng Lingfeng Wang Min Zhang Cheng Zeng Yan Meng |
| author_sort | Shijie Cheng |
| collection | DOAJ |
| description | As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based GAN (U-Net GAN), a recently developed approach, can generate high-quality synthetic images by using a U-Net architecture for the discriminator. However, this model may suffer from severe mode collapse. In this study, a stable U-Net GAN (SUGAN) is proposed to mainly solve this problem. First, a gradient normalization module is introduced to the discriminator of U-Net GAN. This module effectively reduces gradient magnitudes, thereby greatly alleviating the problems of gradient instability and overfitting. As a result, the training stability of the GAN model is improved. Additionally, in order to solve the problem of blurred edges of the generated images, a modified residual network is used in the generator. This modification enhances its ability to capture image details, leading to higher-definition generated images. Extensive experiments conducted on several datasets show that the proposed SUGAN significantly improves over the Inception Score (IS) and Fréchet Inception Distance (FID) metrics compared with several state-of-the-art and classic GANs. The training process of our SUGAN is stable, and the quality and diversity of the generated samples are higher. This clearly demonstrates the effectiveness of our approach for image generation tasks. The source code and trained model of our SUGAN have been publicly released. |
| first_indexed | 2024-03-10T23:13:32Z |
| format | Article |
| id | doaj.art-8fd39d68eedf402db37862b37511c4a1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:32Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8fd39d68eedf402db37862b37511c4a12023-11-19T08:48:32ZengMDPI AGSensors1424-82202023-08-012317733810.3390/s23177338SUGAN: A Stable U-Net Based Generative Adversarial NetworkShijie Cheng0Lingfeng Wang1Min Zhang2Cheng Zeng3Yan Meng4School of Artificial Intelligence, Hubei University, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaSchool of Artificial Intelligence, Hubei University, Wuhan 430062, ChinaSchool of Artificial Intelligence, Hubei University, Wuhan 430062, ChinaAs one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based GAN (U-Net GAN), a recently developed approach, can generate high-quality synthetic images by using a U-Net architecture for the discriminator. However, this model may suffer from severe mode collapse. In this study, a stable U-Net GAN (SUGAN) is proposed to mainly solve this problem. First, a gradient normalization module is introduced to the discriminator of U-Net GAN. This module effectively reduces gradient magnitudes, thereby greatly alleviating the problems of gradient instability and overfitting. As a result, the training stability of the GAN model is improved. Additionally, in order to solve the problem of blurred edges of the generated images, a modified residual network is used in the generator. This modification enhances its ability to capture image details, leading to higher-definition generated images. Extensive experiments conducted on several datasets show that the proposed SUGAN significantly improves over the Inception Score (IS) and Fréchet Inception Distance (FID) metrics compared with several state-of-the-art and classic GANs. The training process of our SUGAN is stable, and the quality and diversity of the generated samples are higher. This clearly demonstrates the effectiveness of our approach for image generation tasks. The source code and trained model of our SUGAN have been publicly released.https://www.mdpi.com/1424-8220/23/17/7338generative adversarial networkimage generationmode collapsetraining stabilitygradient normalization |
| spellingShingle | Shijie Cheng Lingfeng Wang Min Zhang Cheng Zeng Yan Meng SUGAN: A Stable U-Net Based Generative Adversarial Network Sensors generative adversarial network image generation mode collapse training stability gradient normalization |
| title | SUGAN: A Stable U-Net Based Generative Adversarial Network |
| title_full | SUGAN: A Stable U-Net Based Generative Adversarial Network |
| title_fullStr | SUGAN: A Stable U-Net Based Generative Adversarial Network |
| title_full_unstemmed | SUGAN: A Stable U-Net Based Generative Adversarial Network |
| title_short | SUGAN: A Stable U-Net Based Generative Adversarial Network |
| title_sort | sugan a stable u net based generative adversarial network |
| topic | generative adversarial network image generation mode collapse training stability gradient normalization |
| url | https://www.mdpi.com/1424-8220/23/17/7338 |
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