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In addition, I2I translation is applied in real\u2010world tasks such as image synthesis, super\u2010resolution, virtual fitting, and virtual live streaming. Traditional I2I translation models demonstrate strong performance when handling similar datasets. However, when the domain distance between two datasets is large, translation quality may degrade significantly due to notable differences in image shape and edges. To address this issue, we propose Long\u2010Domain Search GAN (\n                    <jats:bold>LDSGAN<\/jats:bold>\n                    ), an unsupervised I2I translation network that employs a GAN structure as its backbone, incorporating a novel Real\u2010Time Routing Search (\n                    <jats:bold>RTRS<\/jats:bold>\n                    ) module and Sketch Loss. Specifically, RTRS aids in expanding the search space within the target domain, aligning feature projection with images closest to the optimization target. 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