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.

本文提出了一种名为动态无监督适应 (Dynamic Unsupervised Adaptation) 的方法，通过持续适应批规范化层的统计数据，可以在只有一小部分无标签数据的情况下，对一个已经训练好的模型进行继续适应，并取得与强基线相当的性能表现，尤其适应于具有数据稀缺或需要不断适应的情况，例如在困难的天气条件下的自动驾驶对象识别任务。