Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data augmentation, our key insight is that while the translated images between domains may differ in styles, their predictions for the task should be consistent. We exploit this property and introduce a cross-domain consistency loss that enforces our adapted model to produce consistent predictions. Through extensive experimental results, we show that our method compares favorably against the state-of-the-art on a wide variety of unsupervised domain adaptation tasks.

本文提出了一种新颖的基于像素的对抗域自适应算法，通过利用图像到图像翻译方法进行数据增强，并引入跨域一致性损失来强制使得我们的自适应模型产生一致的预测，从而在广泛的无监督域自适应任务中获得与最先进方法相当的表现。

CrDoCo：带有跨域一致性的像素级域转化