  Image to image translation aims to learn a mapping that transforms an image
from one visual domain to another. Recent works assume that images descriptors
can be disentangled into a domain-invariant content representation and a
domain-specific style representation. Thus, translation models seek to preserve
the content of source images while changing the style to a target visual
domain. However, synthesizing new images is extremely challenging especially in
multi-domain translations, as the network has to compose content and style to
generate reliable and diverse images in multiple domains. In this paper we
propose the use of an image retrieval system to assist the image-to-image
translation task. First, we train an image-to-image translation model to map
images to multiple domains. Then, we train an image retrieval model using real
and generated images to find images similar to a query one in content but in a
different domain. Finally, we exploit the image retrieval system to fine-tune
the image-to-image translation model and generate higher quality images. Our
experiments show the effectiveness of the proposed solution and highlight the
contribution of the retrieval network, which can benefit from additional
unlabeled data and help image-to-image translation models in the presence of
scarce data.
