Article | 11-January-2021
GAN. As shown in Figure 1, both the generator and the discriminator add a constraint y, which can be any meaningful information. Input y as an additional input into the generator together with the prior noise z, which affects the generated data. The discriminator will also give a prediction under the influence of the constraint y, to achieve control of the generated samples. The objective optimization function definition of the conditional generative confrontation network is shown in formula 1
Haoyang Liu,
Zhiyi Hu,
Jun Yu,
Shouyi Gao
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 4, 66–74
Article | 30-November-2018
generative model is slow because of the difficulty of generative model modeling. In recent years, until the invention of the most successful generation model--Generative adversarial networks model, this field has been revitalized. Since its introduction, generative adversarial networks has been receiving great attention from the academic and industrial circles. With the rapid development of GAN in theory and model, it has been applied more and more in-depth applications in computer vision, natural
Jie Chen,
Li Zhao
International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 2, 93–98
Article | 24-April-2018
Pengrui Qiu,
Xiping Yuan,
Shu Gan,
Yu Lin
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 114–117
research-article | 13-July-2021
Jie Xiang,
Yuanyuan Yin,
Ziqi Gan,
Sangbeom Shim,
Lixing Zhao
Australasian Orthodontic Journal, Volume 37 , ISSUE 1, 50–61