  In this work, we present a domain flow generation(DLOW) model to bridge two
different domains by generating a continuous sequence of intermediate domains
flowing from one domain to the other. The benefits of our DLOW model are
two-fold. First, it is able to transfer source images into different styles in
the intermediate domains. The transferred images smoothly bridge the gap
between source and target domains, thus easing the domain adaptation task.
Second, when multiple target domains are provided for training, our DLOW model
is also able to generate new styles of images that are unseen in the training
data. We implement our DLOW model based on CycleGAN. A domainness variable is
introduced to guide the model to generate the desired intermediate domain
images. In the inference phase, a flow of various styles of images can be
obtained by varying the domainness variable. We demonstrate the effectiveness
of our model for both cross-domain semantic segmentation and the style
generalization tasks on benchmark datasets. Our implementation is available at
https://github.com/ETHRuiGong/DLOW.
