  This year alone has seen unprecedented leaps in the area of learning-based
image translation, namely CycleGAN, by Zhu et al. But experiments so far have
been tailored to merely two domains at a time, and scaling them to more would
require an quadratic number of models to be trained. And with two-domain models
taking days to train on current hardware, the number of domains quickly becomes
limited by the time and resources required to process them. In this paper, we
propose a multi-component image translation model and training scheme which
scales linearly - both in resource consumption and time required - with the
number of domains. We demonstrate its capabilities on a dataset of paintings by
14 different artists and on images of the four different seasons in the Alps.
Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN
models: a total of 182 generator/discriminator pairs; whereas our model
requires only 14 generator/discriminator pairs.
