  Domain translation is the process of transforming data from one domain to
another while preserving the common semantics. Some of the most popular domain
translation systems are based on conditional generative adversarial networks,
which use source domain data to drive the generator and as an input to the
discriminator. However, this approach does not enforce the preservation of
shared semantics since the conditional input can often be ignored by the
discriminator. We propose an alternative method for conditioning and present a
new framework, where two networks are simultaneously trained, in a supervised
manner, to perform domain translation in opposite directions. Our method is not
only better at capturing the shared information between two domains but is more
generic and can be applied to a broader range of problems. The proposed
framework performs well even in challenging cross-modal translations, such as
video-driven speech reconstruction, for which other systems struggle to
maintain correspondence.
