  The task of unpaired image-to-image translation is highly challenging due to
the lack of explicit cross-domain pairs of instances. We consider here diverse
image translation (DIT), an even more challenging setting in which an image can
have multiple plausible translations. This is normally achieved by explicitly
disentangling content and style in the latent representation and sampling
different styles codes while maintaining the image content. Despite the success
of current DIT models, they are prone to suffer from bias. In this paper, we
study the problem of bias in image-to-image translation. Biased datasets may
add undesired changes (e.g. change gender or race in face images) to the output
translations as a consequence of the particular underlying visual distribution
in the target domain. In order to alleviate the effects of this problem we
propose the use of semantic constraints that enforce the preservation of
desired image properties. Our proposed model is a step towards unbiased diverse
image-to-image translation (UDIT), and results in less unwanted changes in the
translated images while still performing the wanted transformation. Experiments
on several heavily biased datasets show the effectiveness of the proposed
techniques in different domains such as faces, objects, and scenes.
