  In this work, we present an interesting attempt on mixture generation:
absorbing different image concepts (e.g., content and style) from different
domains and thus generating a new domain with learned concepts. In particular,
we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns
concepts of content and style from two domains respectively, and thus can join
them for mixture generation in a new domain, i.e., generating images with
content from one domain and style from another. MIXGAN overcomes the limitation
of current GAN-based models which either generate new images in the same domain
as they observed in training stage, or require off-the-shelf content templates
for transferring or translation. Extensive experimental results demonstrate the
effectiveness of MIXGAN as compared to related state-of-the-art GAN-based
models.
