  Domain adaptation is crucial to adapt a learned model to new scenarios, such
as domain shifts or changing data distributions. Current approaches usually
require a large amount of labeled or unlabeled data from the shifted domain.
This can be a hurdle in fields which require continuous dynamic adaptation or
suffer from scarcity of data, e.g. autonomous driving in challenging weather
conditions. To address this problem of continuous adaptation to distribution
shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously
adapting the statistics of the batch normalization layers we modify the feature
representations of the model. We show that by sequentially adapting a model
with only a fraction of unlabeled data, a strong performance gain can be
achieved. With even less than 1% of unlabeled data from the target domain, DUA
already achieves competitive results to strong baselines. In addition, the
computational overhead is minimal in contrast to previous approaches. Our
approach is simple, yet effective and can be applied to any architecture which
uses batch normalization as one of its components. We show the utility of DUA
by evaluating it on a variety of domain adaptation datasets and tasks including
object recognition, digit recognition and object detection.
