Deep networks are prone to performance degradation when there is a domain
shift between the source (training) data and target (test) data. Recent
test-time adaptation methods update batch normalization layers of pre-trained
source models deployed in new target environments with streaming data to
mitigate such performance degradation. Although such methods can adapt
on-the-fly without first collecting a large target domain dataset, their
performance is dependent on streaming conditions such as mini-batch size and
class-distribution, which can be unpredictable in practice. In this work, we
propose a framework for few-shot domain adaptation to address the practical
challenges of data-efficient adaptation. Specifically, we propose a constrained
optimization of feature normalization statistics in pre-trained source models
supervised by a small support set from the target domain. Our method is easy to
implement and improves source model performance with as few as one sample per
class for classification tasks. Extensive experiments on 5 cross-domain
classification and 4 semantic segmentation datasets show that our method
achieves more accurate and reliable performance than test-time adaptation,
while not being constrained by streaming conditions.

提出了一种少样本领域自适应框架，通过受目标域的一个小支持集合监督的预训练源模型中的特征标准化统计的约束优化，可改善源模型的分类性能，实验证明该方法优于测试时适应方法，并且没有实时流条件的限制。