  We present a mutual information-based framework for unsupervised
image-to-image translation. Our MCMI approach treats single-cycle image
translation models as modules that can be used recurrently in a multi-cycle
translation setting where the translation process is bounded by mutual
information constraints between the input and output images. The proposed
mutual information constraints can improve cross-domain mappings by optimizing
out translation functions that fail to satisfy the Markov property during image
translations. We show that models trained with MCMI produce higher quality
images and learn more semantically-relevant mappings compared to
state-of-the-art image translation methods. The MCMI framework can be applied
to existing unpaired image-to-image translation models with minimum
modifications. Qualitative experiments and a perceptual study demonstrate the
image quality improvements and generality of our approach using several
backbone models and a variety of image datasets.
