  Recent advances in image-to-image translation have led to some ways to
generate multiple domain images through a single network. However, there is
still a limit in creating an image of a target domain without a dataset on it.
We propose a method that expands the concept of `multi-domain' from data to the
loss area and learns the combined characteristics of each domain to dynamically
infer translations of images in mixed domains. First, we introduce
Sym-parameter and its learning method for variously mixed losses while
synchronizing them with input conditions. Then, we propose Sym-parameterized
Generative Network (SGN) which is empirically confirmed of learning mixed
characteristics of various data and losses, and translating images to any
mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40%
snowy.
