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 twofold. First, it is able to transfer source images into a domain flow, which consists of images with smoothly changing distributions from the source to the target domain. The domain flow bridges 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. The new images are shown to be able to mimic different artists to produce a natural blend of multiple art styles. Furthermore, for the semantic segmentation in the adverse weather condition, we take advantage of our DLOW model to generate images with gradually changing fog density, which can be readily used for boosting the segmentation performance when combined with a curriculum learning strategy. We demonstrate the effectiveness of our model on benchmark datasets for different applications, including cross-domain semantic segmentation, style generalization, and foggy scene understanding. Our implementation is available at https://github.com/ETHRuiGong/DLOW.

在这项工作中，我们提出了一个域流生成 (DLOW) 模型，通过生成从一个域流到另一个域的中间域的连续序列来桥接两个不同的域。我们的 DLOW 模型的好处是双重的。首先，它能够将源图像传输到域流中，该域流由从源域到目标域的分布平滑变化的图像组成。域流弥合了源域和目标域之间的差距，从而简化了域适应任务。其次，当为训练提供多个目标域时，我们的 DLOW 模型还能够生成在训练数据中看不到的新样式的图像。新图像显示能够模仿不同的艺术家，以产生多种艺术风格的自然融合。此外，对于恶劣天气条件下的语义分割，我们利用我们的 DLOW 模型生成雾密度逐渐变化的图像，当结合课程学习策略时，可以很容易地用于提高分割性能。我们展示了我们的模型在不同应用的基准数据集上的有效性，包括跨域语义分割、风格泛化和模糊场景理解。我们的实现可在 https://github.com/ETHRuiGong/DLOW 获得。包括跨域语义分割、风格泛化和模糊场景理解。我们的实现可在 https://github.com/ETHRuiGong/DLOW 获得。包括跨域语义分割、风格泛化和模糊场景理解。我们的实现可在 https://github.com/ETHRuiGong/DLOW 获得。