The current unsupervised cross-domain detection methods need source domain data to retrain the detection model in target domain. However, the source domain data may be unavailable due to privacy, decentralization, or computation resource restrictions. A natural idea is to optimize the parameters of the source domain model by self-supervised learning based on pseudo labels. We propose another approach from the viewpoint of noise perturbation without pseudo-labeling. It can be assumed that the source and target domains are actually derived from a domain invariant space through domain-specific perturbations, respectively. A super target domain can be constructed by augmenting more target domain perturbations to the target domain images. The optimal direction of the target domain to the domain invariant space can be approximated as the alignment direction from the super target domain to the target domain. Based on this idea, we propose a novel method called SOAP (SOurce data-free domain Adaptation through domain Perturbation) which can remove domain perturbation from the target domain. The image-level, instance-level, and category consistency regularizations based on Mean Teacher structure are proposed to learn the correct alignment direction. Specifically, the category consistency can also further improve the classification accuracy. Extensive experiments on multiple domain adaptation scenarios demonstrate that SOAP achieves better performance surpassing the baseline (Faster R-CNN) and multiple state-of-the-art domain adaptation methods which need to access source domain data.

中文翻译：

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通过特定域扰动对目标检测器进行源无数据域自适应

当前的无监督跨域检测方法需要源域数据来重新训练目标域中的检测模型。但是，由于隐私、去中心化或计算资源限制，源域数据可能不可用。一个自然的想法是通过基于伪标签的自监督学习来优化源域模型的参数。我们从没有伪标记的噪声扰动的角度提出了另一种方法。可以假设源域和目标域实际上分别通过域特定的扰动从域不变空间中导出。可以通过对目标域图像增加更多的目标域扰动来构建超级目标域。目标域到域不变空间的最优方向可以近似为从超目标域到目标域的对齐方向。基于这个想法，我们提出了一种称为 SOAP（Source data-free domain Adaptation through domain Perturbation）的新方法，它可以从目标域中去除域扰动。提出了基于平均教师结构的图像级、实例级和类别一致性正则化来学习正确的对齐方向。具体来说，类别一致性还可以进一步提高分类准确率。对多域适应场景的大量实验表明，SOAP 实现了超越基线（Faster R-CNN）和多种需要访问源域数据的最先进的域适应方法的更好性能。