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• W3034996963 abstract "We propose an image synthesis approach that provides stratified navigation in the latent code space. With a tiny amount of partial or very low-resolution image, our approach can consistently out-perform state-of-the-art counterparts in terms of generating the closest sampled image to the ground truth. We achieve this through scale-independent editing while expanding scale-specific diversity. Scale-independence is achieved with a nested scale disentanglement loss. Scale-specific diversity is created by incorporating a progressive diversification constraint. We introduce semantic persistency across the scales by sharing common latent codes. Together they provide better control of the image synthesis process. We evaluate the effectiveness of our proposed approach through various tasks, including image outpainting, image superresolution, and cross-domain image translation." @default.
• W3034996963 created "2020-06-19" @default.
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• W3034996963 date "2020-06-01" @default.
• W3034996963 modified "2023-10-16" @default.
• W3034996963 title "Nested Scale-Editing for Conditional Image Synthesis" @default.
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• W3034996963 doi "https://doi.org/10.1109/cvpr42600.2020.00552" @default.
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