  Unpaired multimodal image-to-image translation is a task of translating a
given image in a source domain into diverse images in the target domain,
overcoming the limitation of one-to-one mapping. Existing multimodal
translation models are mainly based on the disentangled representations with an
image reconstruction loss. We propose two approaches to improve multimodal
translation quality. First, we use a content representation from the source
domain conditioned on a style representation from the target domain. Second,
rather than using a typical image reconstruction loss, we design MILO (Mutual
Information LOss), a new stochastically-defined loss function based on
information theory. This loss function directly reflects the interpretation of
latent variables as a random variable. We show that our proposed model Mutual
Information with StOchastic Style Representation(MISO) achieves
state-of-the-art performance through extensive experiments on various
real-world datasets.
