Existing state-of-the-art techniques in exemplar-based image-to-image translation have several critical problems. Existing method related to exemplar-based image-to-image translation is impossible to translate on an image tuple input(source, target) that is not aligned. Also, we can confirm that the existing method has limited generalization ability to unseen images. To overcome this limitation, we propose Multiple GAN Inversion for Exemplar-based Image-to-Image Translation. Our novel Multiple GAN Inversion avoids human intervention using a self-deciding algorithm in choosing the number of layers using Fr\'echet Inception Distance(FID), which selects more plausible image reconstruction result among multiple hypotheses without any training or supervision. Experimental results shows the advantage of the proposed method compared to existing state-of-the-art exemplar-based image-to-image translation methods.

翻译：以立像为主的图像到图像翻译的现有最新技术存在几个关键问题。 与以立像为主的图像到图像翻译相关的现有方法无法在不对齐的图像图普输入( 源、 目标) 上翻译。 此外, 我们还可以确认, 现有方法对隐形图像的概括化能力有限。 为了克服这一限制, 我们建议多GAN 转换为基于立像到图像的Exmplar图像翻译。 我们的小说“ 多式GAN Inversion” 避免使用自定算法来选择使用 Fr\' echet Inception Learway( FID) 的层数, 该算法在不经过任何培训或监督的情况下选择多个假设中更可信的图像重建结果。 实验结果显示, 与现有最先进的基于立像的图像到图像的翻译方法相比,拟议方法具有优势。