Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To resolve the issue, Unsupervised Domain Expansion (UDE) has been proposed recently. UDE tries to adapt the model for the target domain as UDA does, and in the meantime maintains its source-domain performance. In both UDA and UDE settings, a model tailored to a given domain, let it be the source or the target domain, is assumed to well handle samples from the given domain. We question the assumption by reporting the existence of cross-domain visual ambiguity: Given the lack of a crystally clear boundary between the two domains, samples from one domain can be visually close to the other domain. Such sorts of samples are typically in minority in their host domain, so they tend to be overlooked by the domain-specific model, but can be better handled by a model from the other domain. We exploit this finding, and accordingly propose Co-Teaching (CT). The CT method is instantiated with knowledge distillation based CT (kdCT) plus mixup based CT (miCT). Specifically, kdCT transfers knowledge from a leading-teacher network and an assistant-teacher network to a student network, so the cross-domain ambiguity will be better handled by the student. Meanwhile, miCT further enhances the generalization ability of the student. Extensive experiments on two image classification datasets and two driving-scene segmentation datasets justify the viability of CT for UDA and UDE.

翻译：无监督领域适应（UDA）本质上是牺牲模型在源域的性能以提升其在目标域的性能。为解决该问题，近期提出了无监督领域扩展（UDE）。UDE尝试像UDA一样使模型适应目标域，同时保持其在源域的性能。在UDA和UDE两种设置中，针对特定领域（无论是源域还是目标域）定制的模型被假设能良好处理该领域的样本。我们通过报告跨域视觉模糊性的存在来质疑这一假设：由于两个领域之间缺乏清晰界限，来自一个领域的样本可能在视觉上接近另一个领域。这类样本通常在其所属领域中占少数，因此容易被该领域的特定模型忽视，但可被另一领域的模型更好地处理。我们利用这一发现，并据此提出协同教学（Co-Teaching，CT）。CT方法通过基于知识蒸馏的CT（kdCT）和基于混合增强的CT（miCT）实例化。具体而言，kdCT将主导教师网络和辅助教师网络的知识迁移至学生网络，使学生网络能更好地处理跨域模糊性。同时，miCT进一步增强了学生网络的泛化能力。在两个图像分类数据集和两个驾驶场景分割数据集上的大量实验验证了CT在UDA和UDE中的可行性。