Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena
SAGAN allows attention-driven, long-range dependency modeling for image generation tasks.
Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance.
Paper: https://arxiv.org/pdf/1805.08318
GitHub: https://github.com/brain-research/self-attention-gan
#GAN #SAGAN #مقاله #کد
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena
SAGAN allows attention-driven, long-range dependency modeling for image generation tasks.
Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance.
Paper: https://arxiv.org/pdf/1805.08318
GitHub: https://github.com/brain-research/self-attention-gan
#GAN #SAGAN #مقاله #کد
GitHub
GitHub - brain-research/self-attention-gan
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