  We introduce GANHopper, an unsupervised image-to-image translation network
that transforms images gradually between two domains, through multiple hops.
Instead of executing translation directly, we steer the translation by
requiring the network to produce in-between images that resemble weighted
hybrids between images from the input domains. Our network is trained on
unpaired images from the two domains only, without any in-between images. All
hops are produced using a single generator along each direction. In addition to
the standard cycle-consistency and adversarial losses, we introduce a new
hybrid discriminator, which is trained to classify the intermediate images
produced by the generator as weighted hybrids, with weights based on a
predetermined hop count. We also add a smoothness term to constrain the
magnitude of each hop, further regularizing the translation. Compared to
previous methods, GANHopper excels at image translations involving
domain-specific image features and geometric variations while also preserving
non-domain-specific features such as general color schemes.
