  This paper proposes a novel approach to domain translation. Leveraging
established parallels between generative models and dynamical systems, we
propose a reformulation of the Cycle-GAN architecture. By embedding our model
with a Hamiltonian structure, we obtain a continuous, expressive and most
importantly invertible generative model for domain translation.
