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.

翻译：域适应旨在缓解源域与目标域之间的域偏移。大多数域适应方法需要访问源数据，但通常由于数据隐私或知识产权等原因无法实现。本文针对具有挑战性的无源域适应问题，即在无源数据情况下将源预训练模型适配到目标域。我们的方法基于一个观察：目标数据虽然可能与源域分类器不对齐，但仍形成清晰的聚类结构。通过定义目标数据的局部亲和性，我们捕捉这种内在结构，并鼓励具有高局部亲和性的数据保持标签一致性。我们注意到互惠邻居应被赋予更高的亲和性。为聚合更多上下文信息，我们考虑了具有小亲和值的扩展邻域。此外，我们考虑每个目标样本周围的密度，这有助于缓解潜在离群值的负面影响。实验结果验证了目标特征的内在结构是域适应的重要信息源。我们证明通过考虑局部邻域、互惠邻域和扩展邻域可以高效捕捉这种局部结构。最终，我们在多个2D图像和3D点云识别数据集上实现了最先进的性能。