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Article | 11-January-2021

Conditional GAN-based Remote Sensing Target Image Generation Method

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

Image Transformation Based on Generative Adversarial Networks

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

Research on Image Denoising Adaptive Algorithm for UAV Based on Visual Landing

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

Mandibular effects of temporary anchorage devices in Class II patients treated with Forsus Fatigue Resistant Devices: A systematic review

Jie Xiang, Yuanyuan Yin, Ziqi Gan, Sangbeom Shim, Lixing Zhao

Australasian Orthodontic Journal, Volume 37 , ISSUE 1, 50–61

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