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GAN-D: Generative adversarial networks for image deconvolution
2017 International Conference on Information and Communication Technology Convergence (ICTC), 2017We propose new generative adversarial networks for generalized image deconvolution, GAN-D. Most of the previous researches concentrate to specific sub-topic of image deconvolution or generative image deconvolution models with a strong assumption. However, our network restores visual data from distorted images applied multiple dominant degradation ...
Ha Yeon Lee +5 more
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SIP-GAN: Generative Adversarial Networks for SIP traffic generation
2021 International Symposium on Networks, Computers and Communications (ISNCC), 2021Amar Meddahi +2 more
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Generative Adversarial Networks for Face Generation: A Survey
ACM Computing Surveys, 2023Amina Kammoun, Rim Slama, Hedi Tabia
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A Survey on Generative Adversarial Networks: Variants, Applications, and Training
ACM Computing Surveys, 2022Xi Li
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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
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Generative Adversarial Networks
ACM Computing Surveys, 2022Zhipeng Cai, Zuobin Xiong, Honghui Xu
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Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges
IEEE Transactions on Emerging Topics in Computational Intelligence, 2020Zhaoqing Pan, Bosi Wang, Haoran Xie
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