  We propose Mask CycleGAN, a novel architecture for unpaired image domain
translation built based on CycleGAN, with an aim to address two issues: 1)
unimodality in image translation and 2) lack of interpretability of latent
variables. Our innovation in the technical approach is comprised of three key
components: masking scheme, generator and objective. Experimental results
demonstrate that this architecture is capable of bringing variations to
generated images in a controllable manner and is reasonably robust to different
masks.
