Domain Translation via Latent Space Mapping

Tsiry Mayet, Simon Bernard, Clément Chatelain, Romain Hérault

Abstract: In this paper, we study the problem of multi-domain translation: given an element (a) of domain A, we wish to generate a corresponding element (b) in another domain B, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domain to another when supervision is available as a pair (a, b), and leverage possible unpaired data when only (a) or only (b) is available. We introduce a new unified framework called Latent Space Mapping (LSM), which exploits the manifold assumption to learn a latent space from each domain. Unlike existing approaches, we propose to further regularize each latent space using available domains by learning each dependency between pairs of domains. We evaluate our approach on three tasks performing i) a synthetic dataset with image translation, ii) a real-world task of semantic segmentation for medical images, and iii) a real-world task of facial landmark detection.