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- W2890730698 abstract "Unpaired image-to-image translation is a tricky task which aims at learning a mapping from one image collection to another image collection without any pair-labeled information. Recent works have proposed cycle-consistency assumption to deal with this task. However, the result is still unsatisfactory for geometric translation. To address this limitation, this paper proposes a novel method using shared deep space generative adversarial network (SDSGAN). Both two images are encoded into a shared deep space through a pre-trained VGG- 19 network, and then we use two decoders to convert them separately to corresponding image domains. In addition, we introduce skip-connection block and self-reconstruction loss to facilitate the mapping. Experimental results show that the proposed SDSGAN has both numerical and perceptual superiorities to existing methods." @default.
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- W2890730698 date "2018-10-01" @default.
- W2890730698 modified "2023-10-16" @default.
- W2890730698 title "Unpaired Image-to-Image Translation from Shared Deep Space" @default.
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- W2890730698 doi "https://doi.org/10.1109/icip.2018.8451250" @default.
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