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- W2963567641 abstract "This paper investigates a novel problem of generating images from visual attributes. We model the image as a composite of foreground and background and develop a layered generative model with disentangled latent variables that can be learned end-to-end using a variational auto-encoder. We experiment with natural images of faces and birds and demonstrate that the proposed models are capable of generating realistic and diverse samples with disentangled latent representations. We use a general energy minimization algorithm for posterior inference of latent variables given novel images. Therefore, the learned generative models show excellent quantitative and visual results in the tasks of attribute-conditioned image reconstruction and completion." @default.
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- W2963567641 date "2016-01-01" @default.
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- W2963567641 title "Attribute2Image: Conditional Image Generation from Visual Attributes" @default.
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- W2963567641 doi "https://doi.org/10.1007/978-3-319-46493-0_47" @default.
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