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• W2926152368 abstract "Image to image translation is the problem of transferring an image from a source domain to a target domain. We present a new method to transfer the underlying semantics of an image even when there are geometric changes across the two domains. Specifically, we present a Generative Adversarial Network (GAN) that can transfer semantic information presented as segmentation masks. Our main technical contribution is an encoder-decoder based generator architecture that jointly encodes the image and its underlying semantics and translates both simultaneously to the target domain. Additionally, we propose object transfiguration and cross-domain semantic consistency losses that preserve the underlying semantic labels maps. We demonstrate the effectiveness of our approach in multiple object transfiguration and domain transfer tasks through qualitative and quantitative experiments. The results show that our method is better at transferring image semantics than state of the art image to image translation methods." @default.
• W2926152368 created "2019-04-11" @default.
• W2926152368 creator A5033839227 @default.
• W2926152368 creator A5046044122 @default.
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• W2926152368 date "2019-04-03" @default.
• W2926152368 modified "2023-09-26" @default.
• W2926152368 title "Semantics-Aware Image to Image Translation and Domain Transfer." @default.
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• W2926152368 hasPublicationYear "2019" @default.
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