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.

本文介绍了一种名为 ModularGAN 的多领域图像生成和图像到图像转换的方法，利用可复用和可组合的不同功能模块（例如编码、解码、变换）同时训练，并在测试时根据具体的图像转换任务组合这些模块，以显著提高生成（或转换为）任何所需领域图像的灵活性，并在多领域面部属性转移方面胜过现有方法。