论文标题
CRDOCO：具有跨域一致性的像素级域转移
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
论文作者
论文摘要
无监督的域适应算法旨在将所学的知识从一个域转移到另一个领域（例如，合成至真实图像）。适应的表示通常不会捕获对密集预测任务至关重要的像素级域移动（例如语义分割）。在本文中，我们提出了一种新颖的像素对敌方域适应算法。通过利用图像到图像翻译方法进行数据增强，我们的主要见解是，尽管域之间的翻译图像可能会有所不同，但它们对任务的预测应该是一致的。我们利用这一属性并引入了跨域一致性损失，该损失可以实施我们的改编模型以产生一致的预测。通过广泛的实验结果，我们表明我们的方法与各种无监督的域适应任务上的最新方法进行了比较。
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.
