  In this paper, we present DRANet, a network architecture that disentangles
image representations and transfers the visual attributes in a latent space for
unsupervised cross-domain adaptation. Unlike the existing domain adaptation
methods that learn associated features sharing a domain, DRANet preserves the
distinctiveness of each domain's characteristics. Our model encodes individual
representations of content (scene structure) and style (artistic appearance)
from both source and target images. Then, it adapts the domain by incorporating
the transferred style factor into the content factor along with learnable
weights specified for each domain. This learning framework allows
bi-/multi-directional domain adaptation with a single encoder-decoder network
and aligns their domain shift. Additionally, we propose a content-adaptive
domain transfer module that helps retain scene structure while transferring
style. Extensive experiments show our model successfully separates
content-style factors and synthesizes visually pleasing domain-transferred
images. The proposed method demonstrates state-of-the-art performance on
standard digit classification tasks as well as semantic segmentation tasks.
