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• W3152912388 abstract "In recent years, the use of Generative Adversarial Networks (GANs) has become very popular in generative image modeling. While style-based GAN architectures yield state-of-the-art results in high-fidelity image synthesis, computationally, they are highly complex. In our work, we focus on the performance optimization of style-based generative models. We analyze the most computationally hard parts of StyleGAN2, and propose changes in the generator network to make it possible to deploy style-based generative networks in the edge devices. We introduce MobileStyleGAN architecture, which has x3.5 fewer parameters and is x9.5 less computationally complex than StyleGAN2, while providing comparable quality." @default.
• W3152912388 created "2021-04-26" @default.
• W3152912388 creator A5029359956 @default.
• W3152912388 date "2021-04-10" @default.
• W3152912388 modified "2023-09-27" @default.
• W3152912388 title "MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis" @default.
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• W3152912388 hasPublicationYear "2021" @default.
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