Domain adaptation is of huge interest as labeling is an expensive and error-prone task, especially when labels are needed on pixel-level like in semantic segmentation. Therefore, one would like to be able to train neural networks on synthetic domains, where data is abundant and labels are precise. However, these models often perform poorly on out-of-domain images. To mitigate the shift in the input, image-to-image approaches can be used. Nevertheless, standard image-to-image approaches that bridge the domain of deployment with the synthetic training domain do not focus on the downstream task but only on the visual inspection level. We therefore propose a "task aware" version of a GAN in an image-to-image domain adaptation approach. With the help of a small amount of labeled ground truth data, we guide the image-to-image translation to a more suitable input image for a semantic segmentation network trained on synthetic data (synthetic-domain expert). The main contributions of this work are 1) a modular semi-supervised domain adaptation method for semantic segmentation by training a downstream task aware CycleGAN while refraining from adapting the synthetic semantic segmentation expert 2) the demonstration that the method is applicable to complex domain adaptation tasks and 3) a less biased domain gap analysis by using from scratch networks. We evaluate our method on a classification task as well as on semantic segmentation. Our experiments demonstrate that our method outperforms CycleGAN - a standard image-to-image approach - by 7 percent points in accuracy in a classification task using only 70 (10%) ground truth images. For semantic segmentation we can show an improvement of about 4 to 7 percent points in mean Intersection over union on the Cityscapes evaluation dataset with only 14 ground truth images during training.

翻译：由于标签是一项昂贵且易出错的任务, 特别是当像素级标签需要像语义区段那样的像素级标签时, 域的适应非常有意义。 因此, 人们希望能够对合成域的神经网络进行培训, 因为在合成域域中, 数据丰富且标签精确。 然而, 这些模型通常在外域图像上表现不佳 。 为了减轻输入的转变, 只能使用图像到图像的方法。 然而, 标准图像到图像的方法可以将部署域与合成培训域连接起来, 特别是当需要标签的像素级, 而不是以视觉检查级别为重点时。 因此, 我们提议在图像到图像区段适应方法上, 在图像到外域域中, 将图像到图像转换转换到更合适的输入图象图象。 在合成数据分类中, 只能使用合成数据( 合成- 数字- 数据学培训专家 ) 这项工作的主要贡献是, 一个模块化的半超超值域域域域校程调整方法, 用于图像路段的图像路段调整, 将数据转换成一个常规路段, 系统路段 。