Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration from the concept of stability from the control theory that a robust system requires to remain consistent both externally and internally regardless of disturbances, we propose a novel framework that achieves unsupervised domain adaptive detection through stability analysis. In specific, we treat discrepancies between images and regions from different domains as disturbances, and introduce a novel simple but effective Network Stability Analysis (NSA) framework that considers various disturbances for domain adaptation. Particularly, we explore three types of perturbations including heavy and light image-level disturbances and instancelevel disturbance. For each type, NSA performs external consistency analysis on the outputs from raw and perturbed images and/or internal consistency analysis on their features, using teacher-student models. By integrating NSA into Faster R-CNN, we immediately achieve state-of-the-art results. In particular, we set a new record of 52.7% mAP on Cityscapes-to-FoggyCityscapes, showing the potential of NSA for domain adaptive detection. It is worth noticing, our NSA is designed for general purpose, and thus applicable to one-stage detection model (e.g., FCOS) besides the adopted one, as shown by experiments. https://github.com/tiankongzhang/NSA.

翻译：[translated abstract in Chinese] 域自适应检测旨在提升检测器（基于有标签源域训练得到）在无标签目标域上的泛化能力。受控制论中稳定性的启发——鲁棒系统需在外部干扰下保持内外一致性，本文提出一种新颖框架，通过稳定性分析实现无监督域自适应检测。具体而言，我们将不同域间图像与区域的差异视为扰动，并引入简单而有效的网络稳定性分析框架，综合考虑多种扰动进行域自适应。我们重点探索了三类扰动：强/弱图像级扰动与实例级扰动。针对每类扰动，NSA通过师生模型对原始图像与扰动图像的输出进行外部一致性分析，或对其特征进行内部一致性分析。将NSA集成至Faster R-CNN后，我们立即取得了最先进的结果。特别地，我们在Cityscapes-to-FoggyCityscapes任务上创造了52.7% mAP的新纪录，展现了NSA在域自适应检测中的潜力。值得关注的是，NSA被设计为通用框架，除所采用的两阶段检测模型外，亦可应用于单阶段检测模型（如FCOS），实验已证实其普适性。代码开源地址：https://github.com/tiankongzhang/NSA。