VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework
Image style transfer is a challenging task that has gained significant attention in recent years due to its growing complexity. Training is typically performed using paradigms offered by GAN-based image style transfer networks. Cycle-based training methods provide an approach for handling unpaired d...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2671 |
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| author | Dawei Guan Xinping Lin Haoyi Zhang Hang Zhou |
| author_facet | Dawei Guan Xinping Lin Haoyi Zhang Hang Zhou |
| author_sort | Dawei Guan |
| collection | DOAJ |
| description | Image style transfer is a challenging task that has gained significant attention in recent years due to its growing complexity. Training is typically performed using paradigms offered by GAN-based image style transfer networks. Cycle-based training methods provide an approach for handling unpaired data. Nevertheless, achieving high transfer quality remains a challenge with these methods due to the simplicity of the employed network. The purpose of this research is to present <i>VariGAN</i>, a novel approach that incorporates three additional strategies to optimize GAN-based image style transfer: (1) Improving the quality of transferred images by utilizing an effective UNet generator network in conjunction with a context-related feature extraction module. (2) Optimizing the training process while reducing dependency on the generator through the use of a depthwise discriminator. (3) Introducing LPIPS loss to further refine the loss function and enhance the overall generation quality of the framework. Through a series of experiments, we demonstrate that the <i>VariGAN</i> backbone exhibits superior performance across diverse content and style domains. <i>VariGAN</i> improved class IoU by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>236</mn><mo>%</mo></mrow></semantics></math></inline-formula> and participant identification by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>195</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to <i>CycleGAN</i>. |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-b5a1d587c9274e8f924a1f0cd5db87d22025-08-20T01:49:11ZengMDPI AGSensors1424-82202025-04-01259267110.3390/s25092671VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning FrameworkDawei Guan0Xinping Lin1Haoyi Zhang2Hang Zhou3School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaImage style transfer is a challenging task that has gained significant attention in recent years due to its growing complexity. Training is typically performed using paradigms offered by GAN-based image style transfer networks. Cycle-based training methods provide an approach for handling unpaired data. Nevertheless, achieving high transfer quality remains a challenge with these methods due to the simplicity of the employed network. The purpose of this research is to present <i>VariGAN</i>, a novel approach that incorporates three additional strategies to optimize GAN-based image style transfer: (1) Improving the quality of transferred images by utilizing an effective UNet generator network in conjunction with a context-related feature extraction module. (2) Optimizing the training process while reducing dependency on the generator through the use of a depthwise discriminator. (3) Introducing LPIPS loss to further refine the loss function and enhance the overall generation quality of the framework. Through a series of experiments, we demonstrate that the <i>VariGAN</i> backbone exhibits superior performance across diverse content and style domains. <i>VariGAN</i> improved class IoU by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>236</mn><mo>%</mo></mrow></semantics></math></inline-formula> and participant identification by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>195</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to <i>CycleGAN</i>.https://www.mdpi.com/1424-8220/25/9/2671style transferGANLPIPSUNetResNetimage segmentation |
| spellingShingle | Dawei Guan Xinping Lin Haoyi Zhang Hang Zhou VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework Sensors style transfer GAN LPIPS UNet ResNet image segmentation |
| title | VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework |
| title_full | VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework |
| title_fullStr | VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework |
| title_full_unstemmed | VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework |
| title_short | VariGAN: Enhancing Image Style Transfer via UNet Generator, Depthwise Discriminator, and LPIPS Loss in Adversarial Learning Framework |
| title_sort | varigan enhancing image style transfer via unet generator depthwise discriminator and lpips loss in adversarial learning framework |
| topic | style transfer GAN LPIPS UNet ResNet image segmentation |
| url | https://www.mdpi.com/1424-8220/25/9/2671 |
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