  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.
