Image-to-image translation is a general name for a task where an image from
one domain is converted to a corresponding image in another domain, given
sufficient training data. Traditionally different approaches have been proposed
depending on whether aligned image pairs or two sets of (unaligned) examples
from both domains are available for training. While paired training samples
might be difficult to obtain, the unpaired setup leads to a highly
under-constrained problem and inferior results. In this paper, we propose a new
general purpose image-to-image translation model that is able to utilize both
paired and unpaired training data simultaneously. We compare our method with
two strong baselines and obtain both qualitatively and quantitatively improved
results. Our model outperforms the baselines also in the case of purely paired
and unpaired training data. To our knowledge, this is the first work to
consider such hybrid setup in image-to-image translation.

本文提出了一种新的通用的图像到图像转换模型，可以同时利用配对和未配对数据进行训练，相比于传统方法性能得到显著提升，此外，研究中首次考虑了混合配对和未配对数据的情况。