  Image-to-image translation has recently received significant attention due to
advances in deep learning. Most works focus on learning either a one-to-one
mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised
way, which is harder due to the lack of supervision and the complex inner- and
cross-domain variations. To alleviate these issues, we propose the Exemplar
Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network
which conditions the translation process on an exemplar image in the target
domain. We assume that an image comprises of a content component which is
shared across domains, and a style component specific to each domain. Under the
guidance of an exemplar from the target domain we apply Adaptive Instance
Normalization to the shared content component, which allows us to transfer the
style information of the target domain to the source domain. To avoid semantic
inconsistencies during translation that naturally appear due to the large
inner- and cross-domain variations, we introduce the concept of feature masks
that provide coarse semantic guidance without requiring the use of any semantic
labels. Experimental results on various datasets show that EGSC-IT does not
only translate the source image to diverse instances in the target domain, but
also preserves the semantic consistency during the process.
