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- W2953190037 abstract "Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change. Inspired by semantic segmentation, we introduce a discriminator with dilated convolutions that is able to use information from across the entire image to train a more context-aware generator. This is coupled with a multi-scale perceptual loss that is better able to represent error in the underlying shape of objects. We demonstrate that this design is more capable of representing shape deformation in a challenging toy dataset, plus in complex mappings with significant dataset variation between humans, dolls, and anime faces, and between cats and dogs." @default.
- W2953190037 created "2019-06-27" @default.
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- W2953190037 date "2018-08-13" @default.
- W2953190037 modified "2023-09-23" @default.
- W2953190037 title "Improving Shape Deformation in Unsupervised Image-to-Image Translation" @default.
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