信任你的好朋友：通过互惠邻域聚类的无源域适应
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
AI总结：
针对无源域适应问题，提出利用目标数据局部聚类结构，通过互惠邻域聚类和扩展邻域聚合信息，实现高效域适应并在多个2D和3D数据集上达到最优性能。
摘要：
域适应（DA）旨在缓解源域与目标域之间的域偏移。大多数DA方法需要访问源数据，但通常这并不可行（例如，由于数据隐私或知识产权问题）。在本文中，我们解决了具有挑战性的无源域适应（SFDA）问题，即在没有源数据的情况下，将源预训练模型适应到目标域。我们的方法基于这样一个观察：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。我们通过定义目标数据的局部亲和性来捕获这种内在结构，并鼓励具有高局部亲和性的数据之间保持标签一致性。我们观察到，更高的亲和性应分配给互惠邻居。为了聚合更多上下文信息，我们考虑了具有较小亲和性值的扩展邻域。此外，我们考虑了每个目标样本周围的密度，这可以减轻潜在离群值的负面影响。在实验结果中，我们验证了目标特征的固有结构是域适应的重要信息来源。我们证明了通过考虑局部邻居、互惠邻居和扩展邻域，可以高效地捕获这种局部结构。最后，我们在多个2D图像和3D点云识别数据集上取得了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
