Domain adaptation (DA) aims to alleviate the domain shift between source
domain and target domain. Most DA methods require access to the source data,
but often that is not possible (e.g. due to data privacy or intellectual
property). In this paper, we address the challenging source-free domain
adaptation (SFDA) problem, where the source pretrained model is adapted to the
target domain in the absence of source data. Our method is based on the
observation that target data, which might not align with the source domain
classifier, still forms clear clusters. We capture this intrinsic structure by
defining local affinity of the target data, and encourage label consistency
among data with high local affinity. We observe that higher affinity should be
assigned to reciprocal neighbors. To aggregate information with more context,
we consider expanded neighborhoods with small affinity values. Furthermore, we
consider the density around each target sample, which can alleviate the
negative impact of potential outliers. In the experimental results we verify
that the inherent structure of the target features is an important source of
information for domain adaptation. We demonstrate that this local structure can
be efficiently captured by considering the local neighbors, the reciprocal
neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art
performance on several 2D image and 3D point cloud recognition datasets.

通过定义目标数据的局部关联性并在具有高局部关联性的数据中鼓励标签一致性，基于目标数据的内在结构，以及通过考虑局部邻居、相互邻居和扩展邻域来有效捕捉局部结构，实现了若干 2D 图像和 3D 点云识别数据集的最先进性能。