In this paper we present a solution to the task of "unsupervised domain
adaptation (UDA) of a given pre-trained semantic segmentation model without
relying on any source domain representations". Previous UDA approaches for
semantic segmentation either employed simultaneous training of the model in the
source and target domains, or they relied on an additional network, replaying
source domain knowledge to the model during adaptation. In contrast, we present
our novel Unsupervised BatchNorm Adaptation (UBNA) method, which adapts a given
pre-trained model to an unseen target domain without using -- beyond the
existing model parameters from pre-training -- any source domain
representations (neither data, nor networks) and which can also be applied in
an online setting or using just a few unlabeled images from the target domain
in a few-shot manner. Specifically, we partially adapt the normalization layer
statistics to the target domain using an exponentially decaying momentum
factor, thereby mixing the statistics from both domains. By evaluation on
standard UDA benchmarks for semantic segmentation we show that this is superior
to a model without adaptation and to baseline approaches using statistics from
the target domain only. Compared to standard UDA approaches we report a
trade-off between performance and usage of source domain representations.

本文提出一种新颖的无监督归一化层自适应方法 (Unsupervised BatchNorm Adaptation, UBNA)，该方法通过部分适应归一化层统计来将给定的预训练模型适应到看不见的目标领域，无需使用来自源领域的任何表示 (既没有数据也没有网络)，并且可以以在线方式或者在使用目标领域中的少量未标记图像的情况下，以几次样本的方式进行。通过评估利用标准的无监督领域适应基准的语义分割，我们展示了这比没有适应和使用仅从目标域中获得的统计数据的基线方法优越，并且与标准的无监督领域适应方法相比，我们报告了性能和使用源领域表示之间的权衡。