Domain shift is unavoidable in real-world applications of object detection.
For example, in self-driving cars, the target domain consists of unconstrained
road environments which cannot all possibly be observed in training data.
Similarly, in surveillance applications sufficiently representative training
data may be lacking due to privacy regulations. In this paper, we address the
domain adaptation problem from the perspective of robust learning and show that
the problem may be formulated as training with noisy labels. We propose a
robust object detection framework that is resilient to noise in bounding box
class labels, locations and size annotations. To adapt to the domain shift, the
model is trained on the target domain using a set of noisy object bounding
boxes that are obtained by a detection model trained only in the source domain.
We evaluate the accuracy of our approach in various source/target domain pairs
and demonstrate that the model significantly improves the state-of-the-art on
multiple domain adaptation scenarios on the SIM10K, Cityscapes and KITTI