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 no longer 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, and propose a self
regularization loss to decrease the negative impact of noisy neighbors.
Furthermore, to aggregate information with more context, we consider expanded
neighborhoods with small affinity values. 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. Code
is available in this https URL

本文提出一种基于目标域数据固有结构的源无关领域适应方法，通过定义目标数据的局部亲和力并鼓励具有高局部亲和力的数据标签一致性来解决领域偏移问题，并在几个 2D 图像和 3D 点云识别数据集上取得了最先进的性能。