  Existing methods for multi-domain image-to-image translation (or generation)
attempt to directly map an input image (or a random vector) to an image in one
of the output domains. However, most existing methods have limited scalability
and robustness, since they require building independent models for each pair of
domains in question. This leads to two significant shortcomings: (1) the need
to train exponential number of pairwise models, and (2) the inability to
leverage data from other domains when training a particular pairwise mapping.
Inspired by recent work on module networks, this paper proposes ModularGAN for
multi-domain image generation and image-to-image translation. ModularGAN
consists of several reusable and composable modules that carry on different
functions (e.g., encoding, decoding, transformations). These modules can be
trained simultaneously, leveraging data from all domains, and then combined to
construct specific GAN networks at test time, according to the specific image
translation task. This leads to ModularGAN's superior flexibility of generating
(or translating to) an image in any desired domain. Experimental results
demonstrate that our model not only presents compelling perceptual results but
also outperforms state-of-the-art methods on multi-domain facial attribute
transfer.
