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.

本文介绍了一项有趣的混合生成尝试：通过从不同的领域吸收不同的图像概念（例如内容和样式），从而生成一个学习概念的新领域。MIXGAN 可从两个领域分别学习内容和样式的概念，因此可以将它们加入新领域进行混合生成，即生成来自一个领域的内容和来自另一个领域的样式的图像。与相关的最先进的基于 GAN 的模型相比，MIXGAN 克服了当前基于 GAN 的模型的限制。