Chinese landscape painting has a unique and artistic style, and its drawing
technique is highly abstract in both the use of color and the realistic
representation of objects. Previous methods focus on transferring from modern
photos to ancient ink paintings. However, little attention has been paid to
translating landscape paintings into modern photos. To solve such problems, in
this paper, we (1) propose DLP-GAN (\textbf{D}raw Modern Chinese
\textbf{L}andscape \textbf{P}hotos with \textbf{G}enerative
\textbf{A}dversarial \textbf{N}etwork), an unsupervised cross-domain image
translation framework with a novel asymmetric cycle mapping, and (2) introduce
a generator based on a dense-fusion module to match different translation
directions. Moreover, a dual-consistency loss is proposed to balance the
realism and abstraction of model painting. In this way, our model can draw
landscape photos and sketches in the modern sense. Finally, based on our
collection of modern landscape and sketch datasets, we compare the images
generated by our model with other benchmarks. Extensive experiments including
user studies show that our model outperforms state-of-the-art methods.

提出了 DLP-GAN，一种基于不对称循环映射的跨域图像转换框架，引入了一个基于密集融合模块的生成器以匹配不同的转换方向，并提出了一种双一致性损失用于平衡模型画作的真实感和抽象性，实现了现代意义上的山水照片和素描绘制。通过与其他基准方法比较，基于我们的现代山水和素描数据集，进行了大量实验证明我们的模型优于现有方法。

DLP-GAN：利用生成对抗网络学习绘制现代中国山水照片

DLP-GAN: Learning to Draw Modern Chinese Landscape Photos with  Generative Adversarial Network

We present a novel and unified deep learning framework which is capable of
learning domain-invariant representation from data across multiple domains.
Realized by adversarial training with additional ability to exploit
domain-specific information, the proposed network is able to perform continuous
cross-domain image translation and manipulation, and produces desirable output
images accordingly. In addition, the resulting feature representation exhibits
superior performance of unsupervised domain adaptation, which also verifies the
effectiveness of the proposed model in learning disentangled features for
describing cross-domain data.

本研究提出了一种新型的深度学习框架，能够从多个领域的数据中学习到不变的表示，通过对抗性训练和利用特定领域信息的能力，实现持续的跨领域图像转换和操纵，并相应地产生期望的输出图像。此外，所得到的特征表示在无监督域自适应方面表现出卓越的性能，也证明了该模型学习描绘跨领域数据的分离特征的有效性。