The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.展开更多
Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and c...Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.展开更多
文摘The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.
基金funded by the Key R&D and Transformation Projects of Xizang(Tibet)Autonomous Region Science and Technology Program(funder:the Department of Science and Technology of the Xizang(Tibet)Autonomous Region),funding(grant)number:XZ202401ZY0004.
文摘Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.
