  Unsupervised image-to-image translation aims at learning the mapping from the
source to target domain without using paired images for training. An essential
yet restrictive assumption for unsupervised image translation is that the two
domains are aligned, e.g., for the selfie2anime task, the anime (selfie) domain
must contain only anime (selfie) face images that can be translated to some
images in the other domain. Collecting aligned domains can be laborious and
needs lots of attention. In this paper, we consider the task of image
translation between two unaligned domains, which may arise for various possible
reasons. To solve this problem, we propose to select images based on importance
reweighting and develop a method to learn the weights and perform translation
simultaneously and automatically. We compare the proposed method with
state-of-the-art image translation approaches and present qualitative and
quantitative results on different tasks with unaligned domains. Extensive
empirical evidence demonstrates the usefulness of the proposed problem
formulation and the superiority of our method.
