  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.
